University / Industry Partner Name

Average Hours

Course Description

Specialization Course Order

Specialization Description

Subtitle Language

Course Language

Introduction to Accounting Data Analytics and Visualization

University of Illinois at Urbana-Champaign

Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R.\n \nWe’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results.\n \nIn the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations.\n \nIn the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).

Microsoft Excel; Data Analysis; Analysis; Data Visualization; Microsoft Excel Vba; Tableau Software; Software; Pivot Table; Regression; Analytics

Accounting Data Analytics

This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

University of Illinois at Urbana-Champaign

This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data).\nThe first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. \nThe second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.

Python Programming; Computer Programming; Accounting; Relational Database Management System; Data Structures; Data Type; SQL; Numpy; Databases; Basic Descriptive Statistics

Accounting Data Analytics

This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

Machine Learning for Accounting with Python

University of Illinois at Urbana-Champaign

This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems.\n\nAccounting Data Analytics with Python is a prerequisite for this course. This course is running on the same platform (Jupyter Notebook) as that of the prerequisite course. While Accounting Data Analytics with Python covers data understanding and data preparation in the data analytics process, this course covers the next two steps in the process, modeling and model evaluation. Upon completion of the two courses, students should be able to complete an entire data analytics process with Python.

Data Analysis; Document Classification; Statistical Classification; Regression; Algorithms; Data Clustering Algorithms; Time Series; Hyperparameter; N-Gram; Random Forest

Accounting Data Analytics

This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.

French; Portuguese; Russian; Spanish

University of Illinois at Urbana-Champaign

This capstone is the last course in the Data Analytics in Accountancy Specialization. In this capstone course, you are going to take the knowledge and skills you have acquired from the previous courses and apply them to a real-world problem.\n\nYou will be provided with a loan dataset from Lending Club which is the largest peer-to-peer lending platform. You will explore the characteristics of the features in the dataset through statistical analysis, exploratory data analysis and visualization. You will also create a machine learning model to predict whether a loan will be fully paid or not. Finally, you will construct a portfolio with the help of your analysis. The goal is to create a portfolio that achieves better return than the overall return of all loans on the Lending Club platform.

Accounting; Microsoft Excel; Finance; Data Analysis; Loan Origination; Pre-Qualification (Lending); Black Litterman Model; Testing Maturity Model; Project Finance Model; Multifunding

Accounting Data Analytics

This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.

French; Portuguese; Javanese; Russian; Spanish

University of Colorado Boulder

This course will expose you to the data analytics practices executed in the business world. We will explore such key areas as the analytical process, how data is created, stored, accessed, and how the organization works with data and creates the environment in which analytics can flourish.\n\nWhat you learn in this course will give you a strong foundation in all the areas that support analytics and will help you to better position yourself for success within your organization. You’ll develop skills and a perspective that will make you more productive faster and allow you to become a valuable asset to your organization.\n\nThis course also provides a basis for going deeper into advanced investigative and computational methods, which you have an opportunity to explore in future courses of the Data Analytics for Business specialization.

SQL; Analysis; Data Analysis; Modeling; Databases; Analytics; Data Model; Relational Database; Business Analytics; Business Analysis

Advanced Business Analytics

The Advanced Business Analytics Specialization brings together academic professionals and experienced practitioners to share real world data analytics skills you can use to grow your business, increase profits, and create maximum value for your shareholders. Learners gain practical skills in extracting and manipulating data using SQL code, executing statistical methods for descriptive, predictive, and prescriptive analysis, and effectively interpreting and presenting analytic results.\n\nThe problems faced by decision makers in today’s competitive business environment are complex. Achieve a clear competitive advantage by using data to explain the performance of a business, evaluate different courses of action, and employ a structured approach to business problem-solving.\n\nCheck out a one-minute video about this specialization to learn more!

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

University of Colorado Boulder

Welcome to the second course in the Data Analytics for Business specialization! \n\nThis course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business. \n\nYou’ll also learn how to summarize and visualize datasets using plots so that you can present your results in a compelling and meaningful way. We will use a practical predictive modeling software, XLMiner, which is a popular Excel plug-in. This course is designed for anyone who is interested in using data to gain insights and make better business decisions. The techniques discussed are applied in all functional areas within business organizations including accounting, finance, human resource management, marketing, operations, and strategic planning. \n\nThe expected prerequisites for this course include a prior working knowledge of Excel, introductory level algebra, and basic statistics.

Predictive Modelling; Regression; Analysis; Predictive Analytics; Analytics; Logistic Regression; Supply Chain; Data Analysis; Linear Regression; Modeling

Advanced Business Analytics

The Advanced Business Analytics Specialization brings together academic professionals and experienced practitioners to share real world data analytics skills you can use to grow your business, increase profits, and create maximum value for your shareholders. Learners gain practical skills in extracting and manipulating data using SQL code, executing statistical methods for descriptive, predictive, and prescriptive analysis, and effectively interpreting and presenting analytic results.\n\nThe problems faced by decision makers in today’s competitive business environment are complex. Achieve a clear competitive advantage by using data to explain the performance of a business, evaluate different courses of action, and employ a structured approach to business problem-solving.\n\nCheck out a one-minute video about this specialization to learn more!

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

University of Colorado Boulder

In this course you will learn how to create models for decision making. We will start with cluster analysis, a technique for data reduction that is very useful in market segmentation. You will then learn the basics of Monte Carlo simulation that will help you model the uncertainty that is prevalent in many business decisions. A key element of decision making is to identify the best course of action. Since businesses problems often have too many alternative solutions, you will learn how optimization can help you identify the best option. What is really exciting about this course is that you won’t need to know a computer language or advanced statistics to learn about these predictive and prescriptive analytic models. The Analytic Solver Platform and basic knowledge of Excel is all you’ll need. Learners participating in assignments will be able to get free access to the Analytic Solver Platform.

Analysis; Analytics; Business Analytics; Mathematical Optimization; Data Analysis; Business Analysis; Decision Making; Simulation; Data Clustering Algorithms; Cluster Analysis

Advanced Business Analytics

The Advanced Business Analytics Specialization brings together academic professionals and experienced practitioners to share real world data analytics skills you can use to grow your business, increase profits, and create maximum value for your shareholders. Learners gain practical skills in extracting and manipulating data using SQL code, executing statistical methods for descriptive, predictive, and prescriptive analysis, and effectively interpreting and presenting analytic results.\n\nThe problems faced by decision makers in today’s competitive business environment are complex. Achieve a clear competitive advantage by using data to explain the performance of a business, evaluate different courses of action, and employ a structured approach to business problem-solving.\n\nCheck out a one-minute video about this specialization to learn more!

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

University of Colorado Boulder

The analytical process does not end with models than can predict with accuracy or prescribe the best solution to business problems. Developing these models and gaining insights from data do not necessarily lead to successful implementations. This depends on the ability to communicate results to those who make decisions. Presenting findings to decision makers who are not familiar with the language of analytics presents a challenge. In this course you will learn how to communicate analytics results to stakeholders who do not understand the details of analytics but want evidence of analysis and data. You will be able to choose the right vehicles to present quantitative information, including those based on principles of data visualization. You will also learn how to develop and deliver data-analytics stories that provide context, insight, and interpretation.

Data Visualization; Communication; Presentation; Analytics; Business Analytics; Analysis; Data Analysis; Graphs; Cognitive Bias; Measurement

Advanced Business Analytics

The Advanced Business Analytics Specialization brings together academic professionals and experienced practitioners to share real world data analytics skills you can use to grow your business, increase profits, and create maximum value for your shareholders. Learners gain practical skills in extracting and manipulating data using SQL code, executing statistical methods for descriptive, predictive, and prescriptive analysis, and effectively interpreting and presenting analytic results.\n\nThe problems faced by decision makers in today’s competitive business environment are complex. Achieve a clear competitive advantage by using data to explain the performance of a business, evaluate different courses of action, and employ a structured approach to business problem-solving.\n\nCheck out a one-minute video about this specialization to learn more!

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

University of Colorado Boulder

The analytics process is a collection of interrelated activities that lead to better decisions and to a higher business performance. The capstone of this specialization is designed with the goal of allowing you to experience this process. The capstone project will take you from data to analysis and models, and ultimately to presentation of insights. \n\nIn this capstone project, you will analyze the data on financial loans to help with the investment decisions of an investment company. You will go through all typical steps of a data analytics project, including data understanding and cleanup, data analysis, and presentation of analytical results. \nFor the first week, the goal is to understand the data and prepare the data for analysis. As we discussed in this specialization, data preprocessing and cleanup is often the first step in data analytics projects. Needless to say, this step is crucial for the success of this project. \n\nIn the second week, you will perform some predictive analytics tasks, including classifying loans and predicting losses from defaulted loans. You will try a variety of tools and techniques this week, as the predictive accuracy of different tools can vary quite a bit. It is rarely the case that the default model produced by ASP is the best model possible. Therefore, it is important for you to tune the different models in order to improve the performance.\n\nBeginning in the third week, we turn our attention to prescriptive analytics, where you will provide some concrete suggestions on how to allocate investment funds using analytics tools, including clustering and simulation based optimization. You will see that allocating funds wisely is crucial for the financial return of the investment portfolio.\n\nIn the last week, you are expected to present your analytics results to your clients. Since you will obtain many results in your project, it is important for you to judiciously choose what to include in your presentation. You are also expected to follow the principles we covered in the courses in preparing your presentation.

Business Analytics; Predictive Analytics; Missing Data; Predictive Modelling; Portfolio (Finance); Analytics; C-Ross; Tableau Software; Investment Fund; Cluster Analysis

Advanced Business Analytics

The Advanced Business Analytics Specialization brings together academic professionals and experienced practitioners to share real world data analytics skills you can use to grow your business, increase profits, and create maximum value for your shareholders. Learners gain practical skills in extracting and manipulating data using SQL code, executing statistical methods for descriptive, predictive, and prescriptive analysis, and effectively interpreting and presenting analytic results.\n\nThe problems faced by decision makers in today’s competitive business environment are complex. Achieve a clear competitive advantage by using data to explain the performance of a business, evaluate different courses of action, and employ a structured approach to business problem-solving.\n\nCheck out a one-minute video about this specialization to learn more!

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models.\n\nIn this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks. Through this exercise you'll also be introduced to the most fundamental statistical measures and data visualization technologies.\n\nThis gives you enough knowledge to take over the role of a data engineer in any modern environment. But it gives you also the basis for advancing your career towards data science. \n\nPlease have a look at the full specialization curriculum:\nhttps://www.coursera.org/specializations/advanced-data-science-ibm\n\nIf you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.\n\n\nAfter completing this course, you will be able to:\n•\tDescribe how basic statistical measures, are used to reveal patterns within the data \n•\tRecognize data characteristics, patterns, trends, deviations or inconsistencies, and potential outliers.\n•\tIdentify useful techniques for working with big data such as dimension reduction and feature selection methods \n•\tUse advanced tools and charting libraries to:\n o\timprove efficiency of analysis of big-data with partitioning and parallel analysis \n o\tVisualize the data in an number of 2D and 3D formats (Box Plot, Run Chart, Scatter Plot, Pareto Chart, and Multidimensional Scaling)\n\nFor successful completion of the course, the following prerequisites are recommended: \n•\tBasic programming skills in python\n•\tBasic math\n•\tBasic SQL (you can get it easily from https://www.coursera.org/learn/sql-data-science if needed)\n\nIn order to complete this course, the following technologies will be used:\n(These technologies are introduced in the course as necessary so no previous knowledge is required.)\n•\tJupyter notebooks (brought to you by IBM Watson Studio for free)\n•\tApacheSpark (brought to you by IBM Watson Studio for free)\n•\tPython\n\nWe've been reported that some of the material in this course is too advanced. So in case you feel the same, please have a look at the following materials first before starting this course, we've been reported that this really helps.\n\nOf course, you can give this course a try first and then in case you need, take the following courses / materials. It's free...\n\nhttps://cognitiveclass.ai/learn/spark\n\nhttps://dataplatform.cloud.ibm.com/analytics/notebooks/v2/f8982db1-5e55-46d6-a272-fd11b670be38/view?access_token=533a1925cd1c4c362aabe7b3336b3eae2a99e0dc923ec0775d891c31c5bbbc68\n\nThis course takes four weeks, 4-6h per week

Apache Spark; Apache; General Statistics; Computer Programming; Python Programming; Data Visualization; SQL; Lambda Calculus; Matplotlib; Dimensionality Reduction

Advanced Data Science with IBM

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.\n\nIf you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

Advanced Machine Learning and Signal Processing

>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<<\n\nThis course, Advanced Machine Learning and Signal Processing, is part of the IBM Advanced Data Science Specialization which IBM is currently creating and gives you easy access to the invaluable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines. We’ll learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then we introduce the most popular Machine Learning Frameworks for python Scikit-Learn and SparkML. SparkML is making up the greatest portion of this course since scalability is key to address performance bottlenecks. We learn how to tune the models in parallel by evaluating hundreds of different parameter-combinations in parallel. We’ll continuously use a real-life example from IoT (Internet of Things), for exemplifying the different algorithms. For passing the course you are even required to create your own vibration sensor data using the accelerometer sensors in your smartphone. So you are actually working on a self-created, real dataset throughout the course.\n\nIf you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

Signal Processing; Machine Learning; Wavelets; Human Learning; Apache Spark; Apache; Wavelet Transform; Digital Signal Processing; Feature Engineering; Algorithms

Advanced Data Science with IBM

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.\n\nIf you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

French; Portuguese; Russian; Spanish

>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<<\n\nThis course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs.\n\nIMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018\n\nUsing these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide.\n\nPrerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well.\n\nIf you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

Keras; Deep Learning; Apache; Human Learning; Tensorflow; Apache Spark; Long Short-Term Memory; PyTorch; Artificial Neural Networks; Apache Systemml

Advanced Data Science with IBM

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.\n\nIf you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

This project completer has proven a deep understanding on massive parallel data processing, data exploration and visualization, advanced machine learning and deep learning and how to apply his knowledge in a real-world practical use case where he justifies architectural decisions, proves understanding the characteristics of different algorithms, frameworks and technologies and how they impact model performance and scalability. \n\nPlease note: You are requested to create a short video presentation at the end of the course. This is mandatory to pass. You don't need to share the video in public.

Modeling; Human Learning; Machine Learning; Use Case; Process Modeling; Extract, Transform, Load; Big Data; Python Programming; Data Science; Keras

Advanced Data Science with IBM

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.\n\nIf you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

Russian; Spanish; Arabic; French; Portuguese; Chinese

The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. \nLearners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image.\n\nThe prerequisites for this course are: \n1) Basic knowledge of Python.\n2) Basic linear algebra and probability.\n\nPlease note that this is an advanced course and we assume basic knowledge of machine learning. You should understand:\n1) Linear regression: mean squared error, analytical solution.\n2) Logistic regression: model, cross-entropy loss, class probability estimation.\n3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions.\n4) The problem of overfitting.\n5) Regularization for linear models.\n\nDo you have technical problems? Write to us: coursera@hse.ru

Deep Learning; Artificial Neural Networks; Human Learning; Tensorflow; Convolutional Neural Network; Recurrent Neural Network; Keras; Autoencoder; Language; Natural Language

Advanced Machine Learning

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

How to Win a Data Science Competition: Learn from Top Kagglers

If you want to break into competitive data science, then this course is for you! Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains such as credit, insurance, marketing, natural language processing, sales’ forecasting and computer vision to name a few. At the same time you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm. Pushing each other to the limit can result in better performance and smaller prediction errors. Being able to achieve high ranks consistently can help you accelerate your career in data science.\n\nIn this course, you will learn to analyse and solve competitively such predictive modelling tasks. \n\nWhen you finish this class, you will:\n\n- Understand how to solve predictive modelling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks.\n- Learn how to preprocess the data and generate new features from various sources such as text and images.\n- Be taught advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures or finding nearest neighbors as a means to improve your predictions.\n- Be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data. \n- Gain experience of analysing and interpreting the data. You will become aware of inconsistencies, high noise levels, errors and other data-related issues such as leakages and you will learn how to overcome them. \n- Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance. \n- Master the art of combining different machine learning models and learn how to ensemble. \n- Get exposed to past (winning) solutions and codes and learn how to read them.\n\nDisclaimer : This is not a machine learning course in the general sense. This course will teach you how to get high-rank solutions against thousands of competitors with focus on practical usage of machine learning methods rather than the theoretical underpinnings behind them.\n\nPrerequisites: \n- Python: work with DataFrames in pandas, plot figures in matplotlib, import and train models from scikit-learn, XGBoost, LightGBM.\n- Machine Learning: basic understanding of linear models, K-NN, random forest, gradient boosting and neural networks.\n\nDo you have technical problems? Write to us: coursera@hse.ru

Feature Engineering; Machine Learning; Analysis; Data Analysis; Kaggle; Human Learning; Mathematical Optimization; Feature Extraction; Hyperparameter; Hyperparameter Optimization

Advanced Machine Learning

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine. \nWhen applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money.\nIn six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can automate this workflow and how to speed it up using some advanced techniques. \nWe will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases be found with Bayesian methods.\n\nDo you have technical problems? Write to us: coursera@hse.ru

Bayesian; Bayesian Inference; Mathematical Optimization; Bayesian Optimization; Variational Bayesian Methods; Inference; Markov Chain; Markov Chain Monte Carlo; Chaining; Gaussian Process

Advanced Machine Learning

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

Welcome to the Reinforcement Learning course. \n\nHere you will find out about:\n\n- foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc.\n--- with math & batteries included\n\n- using deep neural networks for RL tasks\n--- also known as "the hype train"\n\n- state of the art RL algorithms\n--- and how to apply duct tape to them for practical problems.\n\n- and, of course, teaching your neural network to play games\n--- because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits.\n\nJump in. It's gonna be fun!\n\nDo you have technical problems? Write to us: coursera@hse.ru

Reinforcement; Reinforcement Learning; Human Learning; Machine Learning; Deep Learning; Markov Decision Process; Temporal Difference Learning; Dynamic Programming; Relative Change And Difference; Hindley Milner Type System

Advanced Machine Learning

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection. \n\nThroughout the lectures, we will aim at finding a balance between traditional and deep learning techniques in NLP and cover them in parallel. For example, we will discuss word alignment models in machine translation and see how similar it is to attention mechanism in encoder-decoder neural networks. Core techniques are not treated as black boxes. On the contrary, you will get in-depth understanding of what’s happening inside. To succeed in that, we expect your familiarity with the basics of linear algebra and probability theory, machine learning setup, and deep neural networks. Some materials are based on one-month-old papers and introduce you to the very state-of-the-art in NLP research.\n\nDo you have technical problems? Write to us: coursera@hse.ru

Natural Language Processing; Natural Language; Language; Tensorflow; Named-Entity Recognition; Modeling; Human Learning; Deep Learning; N-Gram; Topic Model

Advanced Machine Learning

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

End-to-End Machine Learning with TensorFlow on GCP

In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization (https://www.coursera.org/specializations/machine-learning-tensorflow-gcp).\n\nOne of the best ways to review something is to work with the concepts and technologies that you have learned.\n\nSo, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform\n\nPrerequisites:\nBasic SQL, familiarity with Python and TensorFlow\n\n>>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

Tensorflow; Google Cloud Platform; Cloud Computing; Cloud Platforms; Machine Learning; Human Learning; Bigquery; Dataflow; Deployment Environment; Apache

Advanced Machine Learning on Google Cloud

This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order.

French; Portuguese; Russian; Spanish

Johns Hopkins University

Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.

General Statistics; Booting; Exact Test; Odds Ratio; Statistical Hypothesis Testing; Probability; Mathematical Statistics; Delta Method; Relative Risk; Confidence Interval

Advanced Statistics for Data Science

Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression.\n\nThis specialization starts with Mathematical Statistics bootcamps, specifically concepts and methods used in biostatistics applications. These range from probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling.\n\nThis specialization also linear models for data science, starting from understanding least squares from a linear algebraic and mathematical perspective, to statistical linear models, including multivariate regression using the R programming language. These courses will give learners a firm foundation in the linear algebraic treatment of regression modeling, which will greatly augment applied data scientists' general understanding of regression models.\n\nThis specialization requires a fair amount of mathematical sophistication. Basic calculus and linear algebra are required to engage in the content.

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

Advanced Linear Models for Data Science 1: Least Squares

Johns Hopkins University

Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following:\n\n- A basic understanding of linear algebra and multivariate calculus.\n- A basic understanding of statistics and regression models.\n- At least a little familiarity with proof based mathematics.\n- Basic knowledge of the R programming language.\n\nAfter taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.

Linearity; Linear Model; Least Squares; General Statistics; Square (Algebra); Rank (Linear Algebra); Design Matrix; Regression; Matrices; Linear Algebra

Advanced Statistics for Data Science

Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression.\n\nThis specialization starts with Mathematical Statistics bootcamps, specifically concepts and methods used in biostatistics applications. These range from probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling.\n\nThis specialization also linear models for data science, starting from understanding least squares from a linear algebraic and mathematical perspective, to statistical linear models, including multivariate regression using the R programming language. These courses will give learners a firm foundation in the linear algebraic treatment of regression modeling, which will greatly augment applied data scientists' general understanding of regression models.\n\nThis specialization requires a fair amount of mathematical sophistication. Basic calculus and linear algebra are required to engage in the content.

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

Advanced Linear Models for Data Science 2: Statistical Linear Models

Johns Hopkins University

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following:\n\n- A basic understanding of linear algebra and multivariate calculus.\n- A basic understanding of statistics and regression models.\n- At least a little familiarity with proof based mathematics.\n- Basic knowledge of the R programming language.\n\nAfter taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.

Linearity; Modeling; General Statistics; Linear Model; Multivariate Normal Distribution; Rank (Linear Algebra); Square (Algebra); Least Squares; Quadratic Form; Expected Value

Advanced Statistics for Data Science

Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression.\n\nThis specialization starts with Mathematical Statistics bootcamps, specifically concepts and methods used in biostatistics applications. These range from probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling.\n\nThis specialization also linear models for data science, starting from understanding least squares from a linear algebraic and mathematical perspective, to statistical linear models, including multivariate regression using the R programming language. These courses will give learners a firm foundation in the linear algebraic treatment of regression modeling, which will greatly augment applied data scientists' general understanding of regression models.\n\nThis specialization requires a fair amount of mathematical sophistication. Basic calculus and linear algebra are required to engage in the content.

Russian; Spanish; French; Portuguese; Arabic; Italian; Vietnamese; German

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms, and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required! \n\nThis program will give you practical experience in applying cutting-edge machine learning techniques to concrete problems in modern medicine:\n\n- In Course 1, you will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders. \n- In Course 2, you will build risk models and survival estimators for heart disease using statistical methods and a random forest predictor to determine patient prognosis. \n- In Course 3, you will build a treatment effect predictor, apply model interpretation techniques and use natural language processing to extract information from radiology reports.\n\nThese courses go beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. As a learner, you will be set up for success in this program if you are already comfortable with some of the math and coding behind AI algorithms. You don't need to be an AI expert, but a working knowledge of deep neural networks, particularly convolutional networks, and proficiency in Python programming at an intermediate level will be essential. If you are relatively new to machine learning or neural networks, we recommend that you first take the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng.\n\nThe demand for AI practitioners with the skills and knowledge to tackle the biggest issues in modern medicine is growing exponentially. Join us in this specialization and begin your journey toward building the future of healthcare.

Image Segmentation; Medical Diagnosis; Machine Learning; Statistical Classification; Deep Learning; Medical Imaging; Image Processing; Artificial Neural Networks; Evaluation; Multiclass Classification

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.\n\nThese courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization.

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.\n\nMachine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. In this second course, you’ll walk through multiple examples of prognostic tasks. You’ll then use decision trees to model non-linear relationships, which are commonly observed in medical data, and apply them to predicting mortality rates more accurately. Finally, you’ll learn how to handle missing data, a key real-world challenge. \n\nThese courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. This course focuses on tree-based machine learning, so a foundation in deep learning is not required for this course. However, a foundation in deep learning is highly recommended for course 1 and 3 of this specialization. You can gain a foundation in deep learning by taking the Deep Learning Specialization offered by deeplearning.ai and taught by Andrew Ng.

Machine Learning; General Statistics; Survival Analysis; Analysis; Decision Tree; Modeling; Hazard Function; Prognostics; Random Forest; Euler'S Totient Function

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.\n\nThese courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization.

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.\n\nMedical treatment may impact patients differently based on their existing health conditions. In this third course, you’ll recommend treatments more suited to individual patients using data from randomized control trials. In the second week, you’ll apply machine learning interpretation methods to explain the decision-making of complex machine learning models. Finally, you’ll use natural language entity extraction and question-answering methods to automate the task of labeling medical datasets.\n\nThese courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend that you take the Deep Learning Specialization.

Machine Learning; Interpretation; Average Treatment Effect; Medical Imaging; Deep Learning; Modeling; Estimation; Average; Measurement; System U

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.\n\nThese courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization.

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project.\n \nThis course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

Machine Learning; Ethics; Ethics Of Artificial Intelligence; Artificial General Intelligence; Artificial Neural Networks; Human Learning; Deep Learning; Application Domain; Computer Vision; Cloud Computing

AI Foundations for Everyone

Artificial Intelligence (AI) is no longer science fiction. It is rapidly permeating all industries and having a profound impact on virtually every aspect of our existence. Whether you are an executive, a leader, an industry professional, a researcher, or a student - understanding AI, its impact and transformative potential for your organization and our society is of paramount importance.\n\nThis specialization is designed for those with little or no background in AI, whether you have technology background or not, and does not require any programming skills. It is designed to give you a firm understanding of what is AI, its applications and use cases across various industries. You will become acquainted with terms like Machine Learning, Deep Learning and Neural Networks.\n\nFurthermore, it will familiarize you with IBM Watson AI services that enable any business to quickly and easily employ pre-built AI smarts to their products and solutions. You will also learn about creating intelligent virtual assistants and how they can be leveraged in different scenarios.\n\nBy the end of this specialization, learners will have had hands-on interactions with several AI environments and applications, and have built and deployed an AI enabled chatbot on a website – without any coding.

German; Russian; Spanish; French; Portuguese; Italian; Vietnamese; Arabic

In this course you will learn how to quickly and easily get started with Artificial Intelligence using IBM Watson. You will understand how Watson works, become familiar with its use cases and real life client examples, and be introduced to several of Watson AI services from IBM that enable anyone to easily apply AI and build smart apps. You will also work with several Watson services to demonstrate AI in action.\n \nThis course does not require any programming or computer science expertise and is designed for anyone whether you have a technical background or not.

Machine Learning; Cloud Computing; IBM Cloud; Human Learning; Language; Natural Language; Artificial Neural Networks; Natural Language Processing; Use Case; Opencv

AI Foundations for Everyone

Artificial Intelligence (AI) is no longer science fiction. It is rapidly permeating all industries and having a profound impact on virtually every aspect of our existence. Whether you are an executive, a leader, an industry professional, a researcher, or a student - understanding AI, its impact and transformative potential for your organization and our society is of paramount importance.\n\nThis specialization is designed for those with little or no background in AI, whether you have technology background or not, and does not require any programming skills. It is designed to give you a firm understanding of what is AI, its applications and use cases across various industries. You will become acquainted with terms like Machine Learning, Deep Learning and Neural Networks.\n\nFurthermore, it will familiarize you with IBM Watson AI services that enable any business to quickly and easily employ pre-built AI smarts to their products and solutions. You will also learn about creating intelligent virtual assistants and how they can be leveraged in different scenarios.\n\nBy the end of this specialization, learners will have had hands-on interactions with several AI environments and applications, and have built and deployed an AI enabled chatbot on a website – without any coding.

Russian; Spanish; Arabic; French; Portuguese; Italian; Vietnamese; Korean; German

Este curso presenta una gentil introducción a los conceptos del análisis de datos, el rol de un Analista de Datos y las herramientas que se utilizan para realizar las funciones diarias. Obtendrás una comprensión del ecosistema de datos y de los fundamentos del análisis de datos, como la recopilación de datos o la minería de datos. También aprenderás las aptitudes generales que se requieren para comunicar eficazmente tus datos a los interesados y cómo el dominio de estas aptitudes puede darte la opción de convertirte en un tomador de decisiones impulsado por los datos.\n\nEste curso te ayudará a diferenciar entre los roles de trabajo de un Analista de Datos, un Científico de Datos y un Ingeniero de Datos. Aprenderás las responsabilidades de un Analista de Datos y exactamente lo que implica el análisis de datos. Serás capaz de resumir el ecosistema de datos, como las bases de datos y los almacenes de datos. Luego descubrirás los principales proveedores dentro del ecosistema de datos y explorarás las diversas herramientas en las instalaciones y en la nube. Continúa este emocionante viaje y descubre las plataformas de Grandes Volúmenes de Datos como Hadoop, Hive y Spark. Al final de este curso podrás visualizar la vida diaria de una Analista de Datos, entender las diferentes carreras que están disponibles para el análisis de datos e identificar los muchos recursos disponibles para manejar esta profesión.\n\n A lo largo de este curso aprenderás los aspectos claves del análisis de datos. Empezarás a explorar los fundamentos de la recopilación de datos, y aprenderás a identificar tus fuentes de datos. Luego aprenderás a limpiar, analizar y compartir tus datos con el uso de visualizaciones y herramientas de paneles de datos. Todo esto se combina en el proyecto final donde se pondrán a prueba tus conocimientos del material del curso, explorarás lo que significa ser un Analista de Datos y te proporcionará un escenario del mundo real del análisis de datos.\n\n Este curso no requiere ningún tipo experiencia de análisis de datos, hojas de cálculo o ciencia de la computación. Todo lo que necesitas para comenzar es un conocimiento básico de computación, matemáticas de secundaria y acceso a un navegador web moderno como Chrome o Firefox.

Research Data Archiving; Big Data; Apache Hadoop; Analysis; Data Analysis; Ecosystems; Process; Communication; Extract, Transform, Load; Data Structures

Analista de Datos de IBM

Obtén las habilidades necesarias para un puesto de analista de datos de nivel inicial mediante este Certificado Profesional de IBM, que consta de ocho cursos, y consigue una posición competitiva en el próspero mercado laboral de los analistas de datos, que experimentará un crecimiento del 20% hasta 2028 (Oficina de Estadísticas Laborales de EE.UU.)\n\nImpulsa tu carrera como analista de datos aprendiendo los principios fundamentales de la analítica de datos y adquiriendo conocimientos prácticos. Trabajarás con diversas fuentes de datos, escenarios de proyectos y herramientas de análisis de datos, incluyendo Excel, SQL, Python, Jupyter Notebooks y Cognos Analytics.\n\nEste Certificado Profesional no requiere ningún conocimiento previo de programación o estadística, y es adecuado para estudiantes con o sin títulos universitarios. Todo lo que necesitas para empezar es un conocimiento básico de informática, matemáticas de secundaria, sentirte cómodo trabajando con números, voluntad de aprender y el deseo de enriquecer tu perfil con valiosas habilidades.\n\nUna vez completado con éxito este programa, habrás analizado conjuntos de datos del mundo real, creado cuadros de mando interactivos y presentado informes para compartir tus hallazgos, proporcionándote confianza y un portafolio para comenzar una carrera como analista de datos asociado o junior. También sentarás las bases para otras disciplinas de datos, como la ciencia de los datos o la ingeniería de datos.

Conceptos Básicos de Excel para el Análisis de Datos

Este curso está diseñado para proporcionarle los conocimientos básicos de trabajo para utilizar las hojas de cálculo de Excel para el análisis de datos. Cubre algunos de los primeros pasos para trabajar con hojas de cálculo y su uso en el proceso de análisis de datos. Incluye muchos videos, demostraciones y ejemplos para que aprenda, seguidos de instrucciones paso a paso para que los aplique y practique en una hoja de cálculo en vivo.\n\nExcel es una herramienta esencial para trabajar con datos, ya sea para negocios, marketing, análisis de datos o investigación. Este curso es adecuado para aquellos que aspiran a asumir el análisis de datos o la ciencia de los datos como una profesión, así como aquellos que sólo quieren utilizar Excel para el análisis de datos en sus propios dominios. Obtendrá una valiosa experiencia en la limpieza y la gestión de datos mediante funciones y luego analizará sus datos mediante técnicas como el filtrado, la clasificación y la creación de tablas dinámicas. \n\nEste curso comienza con una introducción a las hojas de cálculo como Microsoft Excel y Google Sheets y la carga de datos de múltiples formatos. Con esta introducción aprenderá a realizar algunas tareas de limpieza y de discusión de datos de nivel básico y continuarás ampliando tus conocimientos sobre el análisis de datos mediante el uso de filtros, clasificación y tablas dinámicas dentro de la hoja de cálculo. Realizando estas tareas a lo largo del curso, le dará una comprensión de cómo las hojas de cálculo pueden ser utilizadas como una herramienta de análisis de datos y comprenderá sus limitaciones. \n\nHay un fuerte enfoque en la práctica y el aprendizaje aplicado en este curso. Con cada laboratorio, obtendrá experiencia práctica en la manipulación de datos y comenzará a comprender el importante papel de las hojas de cálculo. Limpie y analice sus datos más rápido al comprender las funciones en el formato de los datos. A continuación, convertirá sus datos en una tabla pivotante y aprenderá sus características para que sus datos estén organizados y sean legibles. El proyecto final le permite mostrar sus recién adquiridas habilidades de análisis de datos. Al final de este curso usted habrá trabajado con varios conjuntos de datos y hojas de cálculo y demostrado los fundamentos de la limpieza y el análisis de datos, todo ello sin tener que aprender ningún código. \n\nComenzar a usar Excel es fácil en este curso. No requiere ninguna experiencia previa con hojas de cálculo o codificación. Tampoco requiere descargas o instalación de ningún software. Todo lo que se necesita es un dispositivo con un moderno navegador web, y la capacidad de crear una cuenta de Microsoft para acceder a Excel en línea sin costo alguno. Sin embargo, si ya tiene una versión de escritorio de Excel, también puede seguirlo fácilmente.

Pivot Table; Microsoft Excel; Cmos; Data Quality; Spreadsheet; Euler'S Totient Function; Reference Data; Rowing; Worksheet; Standard Score

Analista de Datos de IBM

Obtén las habilidades necesarias para un puesto de analista de datos de nivel inicial mediante este Certificado Profesional de IBM, que consta de ocho cursos, y consigue una posición competitiva en el próspero mercado laboral de los analistas de datos, que experimentará un crecimiento del 20% hasta 2028 (Oficina de Estadísticas Laborales de EE.UU.)\n\nImpulsa tu carrera como analista de datos aprendiendo los principios fundamentales de la analítica de datos y adquiriendo conocimientos prácticos. Trabajarás con diversas fuentes de datos, escenarios de proyectos y herramientas de análisis de datos, incluyendo Excel, SQL, Python, Jupyter Notebooks y Cognos Analytics.\n\nEste Certificado Profesional no requiere ningún conocimiento previo de programación o estadística, y es adecuado para estudiantes con o sin títulos universitarios. Todo lo que necesitas para empezar es un conocimiento básico de informática, matemáticas de secundaria, sentirte cómodo trabajando con números, voluntad de aprender y el deseo de enriquecer tu perfil con valiosas habilidades.\n\nUna vez completado con éxito este programa, habrás analizado conjuntos de datos del mundo real, creado cuadros de mando interactivos y presentado informes para compartir tus hallazgos, proporcionándote confianza y un portafolio para comenzar una carrera como analista de datos asociado o junior. También sentarás las bases para otras disciplinas de datos, como la ciencia de los datos o la ingeniería de datos.

Visualización de Datos y Tableros con Excel y Cognos

Este curso cubre algunos de los primeros pasos en el desarrollo de visualizaciones de datos utilizando hojas de cálculo y tableros. Comienza el proceso de contar una historia con tus datos creando los muchos tipos de gráficos que están disponibles en hojas de cálculo como Excel. Explore las diferentes herramientas de una hoja de cálculo, como la importante función de pivote y la capacidad de crear cuadros de mando, y aprenda cómo cada uno tiene su propia propiedad única para transformar sus datos. Continúe adquiriendo una valiosa experiencia familiarizándose con la popular herramienta analítica - IBM Cognos Analytics - para crear cuadros de mando interactivos.\n\nAl completar este curso, tendrá una comprensión básica del uso de las hojas de cálculo como herramienta de visualización de datos. Adquirirá la capacidad de crear eficazmente visualizaciones de datos, como gráficos o tablas, y comenzará a ver cómo desempeñan un papel fundamental en la comunicación de sus resultados de análisis de datos. Todo esto se puede lograr aprendiendo los fundamentos del análisis de datos con Excel y IBM Cognos Analytics, sin tener que escribir ningún código. Al final de este curso será capaz de describir las herramientas de cuadros de mando comunes utilizadas por un analista de datos, diseñar y crear un cuadro de mando en una plataforma de nube, y comenzar a elevar su nivel de confianza en la creación de visualizaciones de datos de nivel intermedio. \n\nA lo largo de este curso encontrará numerosos laboratorios prácticos y un proyecto final. Con cada laboratorio, gane experiencia práctica en la creación de gráficos básicos y avanzados, y luego continúe a través del curso y comience a crear cuadros de mando con hojas de cálculo y IBM Cognos Analytics. A continuación, terminará este curso creando un conjunto de visualizaciones de datos con IBM Cognos Analytics y creando un cuadro de mando interactivo que podrá compartir con sus compañeros, comunidades profesionales o posibles empleadores.\n\nEste curso no requiere ningún análisis de datos previo, ni experiencia en informática. Todo lo que necesita para empezar es un conocimiento básico de informática, matemáticas de nivel secundario, acceso a un navegador web moderno como Chrome o Firefox, la capacidad de crear una cuenta de Microsoft para acceder a Excel para la Web, y una comprensión básica de las hojas de cálculo de Excel.

Pivot Chart; Chart; Microsoft Excel; Spreadsheet; Cmos; Euler'S Totient Function; Big Data; Peering; Web; Ordered Pair

Analista de Datos de IBM

Obtén las habilidades necesarias para un puesto de analista de datos de nivel inicial mediante este Certificado Profesional de IBM, que consta de ocho cursos, y consigue una posición competitiva en el próspero mercado laboral de los analistas de datos, que experimentará un crecimiento del 20% hasta 2028 (Oficina de Estadísticas Laborales de EE.UU.)\n\nImpulsa tu carrera como analista de datos aprendiendo los principios fundamentales de la analítica de datos y adquiriendo conocimientos prácticos. Trabajarás con diversas fuentes de datos, escenarios de proyectos y herramientas de análisis de datos, incluyendo Excel, SQL, Python, Jupyter Notebooks y Cognos Analytics.\n\nEste Certificado Profesional no requiere ningún conocimiento previo de programación o estadística, y es adecuado para estudiantes con o sin títulos universitarios. Todo lo que necesitas para empezar es un conocimiento básico de informática, matemáticas de secundaria, sentirte cómodo trabajando con números, voluntad de aprender y el deseo de enriquecer tu perfil con valiosas habilidades.\n\nUna vez completado con éxito este programa, habrás analizado conjuntos de datos del mundo real, creado cuadros de mando interactivos y presentado informes para compartir tus hallazgos, proporcionándote confianza y un portafolio para comenzar una carrera como analista de datos asociado o junior. También sentarás las bases para otras disciplinas de datos, como la ciencia de los datos o la ingeniería de datos.

En este curso aprenderá cómo comenzar rápida y fácilmente con la Inteligencia Artificial utilizando IBM Watson. Comprenderá cómo funciona Watson, se familiarizará con sus casos de uso y ejemplos de clientes de la vida real, y se le presentarán varios de los servicios de inteligencia artificial de Watson de IBM que permiten a cualquiera aplicar fácilmente la inteligencia artificial y crear aplicaciones inteligentes. También trabajará con varios servicios de Watson para demostrar la IA en acción.\n \nEste curso no requiere ninguna experiencia en programación o ciencias de la computación y está diseñado para cualquier persona, ya sea que tenga una formación técnica o no.\n\nEsta es una traducción al español de un curso que se creó originalmente en inglés. Muchos de los componentes del curso se han traducido al español, incluidos títulos de lecciones, transcripciones de videos, lecturas, instrucciones de laboratorio y cuestionarios. Sin embargo, algunos componentes del curso, incluidos los videos originales y su narración, todavía están en inglés.

Big Data; Python Programming; Computer Programming; Computer Program; Data Science; Joie De Vivre; Denominación De Origen; Edward De Bono; Cabeza; Gustave Le Bon

Analista de Datos de IBM

Obtén las habilidades necesarias para un puesto de analista de datos de nivel inicial mediante este Certificado Profesional de IBM, que consta de ocho cursos, y consigue una posición competitiva en el próspero mercado laboral de los analistas de datos, que experimentará un crecimiento del 20% hasta 2028 (Oficina de Estadísticas Laborales de EE.UU.)\n\nImpulsa tu carrera como analista de datos aprendiendo los principios fundamentales de la analítica de datos y adquiriendo conocimientos prácticos. Trabajarás con diversas fuentes de datos, escenarios de proyectos y herramientas de análisis de datos, incluyendo Excel, SQL, Python, Jupyter Notebooks y Cognos Analytics.\n\nEste Certificado Profesional no requiere ningún conocimiento previo de programación o estadística, y es adecuado para estudiantes con o sin títulos universitarios. Todo lo que necesitas para empezar es un conocimiento básico de informática, matemáticas de secundaria, sentirte cómodo trabajando con números, voluntad de aprender y el deseo de enriquecer tu perfil con valiosas habilidades.\n\nUna vez completado con éxito este programa, habrás analizado conjuntos de datos del mundo real, creado cuadros de mando interactivos y presentado informes para compartir tus hallazgos, proporcionándote confianza y un portafolio para comenzar una carrera como analista de datos asociado o junior. También sentarás las bases para otras disciplinas de datos, como la ciencia de los datos o la ingeniería de datos.

Korean; English; German

Gran parte de los datos del mundo residen en bases de datos. SQL (o lenguaje de consulta estructurado) es un lenguaje poderoso que se utiliza para comunicarse y extraer datos de bases de datos. Un conocimiento práctico de bases de datos y SQL es imprescindible si desea convertirse en un científico de datos.\n\nEl propósito de este curso es presentar los conceptos de bases de datos relacionales y ayudarlo a aprender y aplicar los conocimientos básicos del lenguaje SQL. También está destinado a ayudarle a empezar a realizar el acceso SQL en un entorno de ciencia de datos.\n\nEl énfasis en este curso está en el aprendizaje práctico y práctico. Como tal, trabajará con bases de datos reales, herramientas de ciencia de datos reales y conjuntos de datos del mundo real. Creará una instancia de base de datos en la nube. A través de una serie de prácticas de laboratorio, practicará la creación y ejecución de consultas SQL. También aprenderá cómo acceder a las bases de datos desde los cuadernos de Jupyter usando SQL y Python.\n\nNo se requieren conocimientos previos de bases de datos, SQL, Python o programación.\n\nCualquiera puede auditar este curso sin cargo. Si elige tomar este curso y obtener el certificado del curso de Coursera, también puede obtener una insignia digital de IBM al completar con éxito el curso.\n\nOFERTA POR TIEMPO LIMITADO: La suscripción cuesta solo $39 USD por mes para acceder a materiales calificados y un certificado.

Databases; SQL; Relational Database; Cloud Computing; Grouped Data; Euler'S Totient Function; Sorting; Ordered Pair; Operations Management; Big Data

Analista de Datos de IBM

Obtén las habilidades necesarias para un puesto de analista de datos de nivel inicial mediante este Certificado Profesional de IBM, que consta de ocho cursos, y consigue una posición competitiva en el próspero mercado laboral de los analistas de datos, que experimentará un crecimiento del 20% hasta 2028 (Oficina de Estadísticas Laborales de EE.UU.)\n\nImpulsa tu carrera como analista de datos aprendiendo los principios fundamentales de la analítica de datos y adquiriendo conocimientos prácticos. Trabajarás con diversas fuentes de datos, escenarios de proyectos y herramientas de análisis de datos, incluyendo Excel, SQL, Python, Jupyter Notebooks y Cognos Analytics.\n\nEste Certificado Profesional no requiere ningún conocimiento previo de programación o estadística, y es adecuado para estudiantes con o sin títulos universitarios. Todo lo que necesitas para empezar es un conocimiento básico de informática, matemáticas de secundaria, sentirte cómodo trabajando con números, voluntad de aprender y el deseo de enriquecer tu perfil con valiosas habilidades.\n\nUna vez completado con éxito este programa, habrás analizado conjuntos de datos del mundo real, creado cuadros de mando interactivos y presentado informes para compartir tus hallazgos, proporcionándote confianza y un portafolio para comenzar una carrera como analista de datos asociado o junior. También sentarás las bases para otras disciplinas de datos, como la ciencia de los datos o la ingeniería de datos.

Arabic; Vietnamese; Korean; English

Aprenda a analizar datos con Python. Este curso lo llevará desde los conceptos básicos de Python hasta la exploración de muchos tipos diferentes de datos. Aprenderá a preparar datos para el análisis, realizar análisis estadísticos simples, crear visualizaciones de datos significativas, predecir tendencias futuras a partir de datos, ¡y más!\n\nTópicos cubiertos:\n\n1) Importación de conjuntos de datos\n2) Limpiar los datos\n3) manipulación del marco de datos\n4) Resumen de los datos\n5) Creación de modelos de regresión de aprendizaje automático\n6) Construcción de canalizaciones de datos\n\n El análisis de datos con Python se entregará a través de conferencias, laboratorio y asignaciones. Incluye las siguientes partes:\n\nBibliotecas de análisis de datos: aprenderá a usar las bibliotecas Pandas, Numpy y Scipy para trabajar con un conjunto de datos de muestra. Le presentaremos pandas, una biblioteca de código abierto, y la usaremos para cargar, manipular, analizar y visualizar conjuntos de datos interesantes. Luego, le presentaremos otra biblioteca de código abierto, scikit-learn, y usaremos algunos de sus algoritmos de aprendizaje automático para construir modelos inteligentes y hacer predicciones interesantes.\n\nSi elige tomar este curso y obtener el certificado del curso de Coursera, también obtendrá una insignia digital de IBM.\n\nOFERTA POR TIEMPO LIMITADO: La suscripción cuesta solo $ 39 USD por mes para acceder a materiales calificados y un certificado.

Python Programming; Modeling; 2.5d; Evaluation; Computer Programming; Summary Statistics; Missing Data; Categorical Data; Pivot Table; Extract, Transform, Load

Analista de Datos de IBM

Obtén las habilidades necesarias para un puesto de analista de datos de nivel inicial mediante este Certificado Profesional de IBM, que consta de ocho cursos, y consigue una posición competitiva en el próspero mercado laboral de los analistas de datos, que experimentará un crecimiento del 20% hasta 2028 (Oficina de Estadísticas Laborales de EE.UU.)\n\nImpulsa tu carrera como analista de datos aprendiendo los principios fundamentales de la analítica de datos y adquiriendo conocimientos prácticos. Trabajarás con diversas fuentes de datos, escenarios de proyectos y herramientas de análisis de datos, incluyendo Excel, SQL, Python, Jupyter Notebooks y Cognos Analytics.\n\nEste Certificado Profesional no requiere ningún conocimiento previo de programación o estadística, y es adecuado para estudiantes con o sin títulos universitarios. Todo lo que necesitas para empezar es un conocimiento básico de informática, matemáticas de secundaria, sentirte cómodo trabajando con números, voluntad de aprender y el deseo de enriquecer tu perfil con valiosas habilidades.\n\nUna vez completado con éxito este programa, habrás analizado conjuntos de datos del mundo real, creado cuadros de mando interactivos y presentado informes para compartir tus hallazgos, proporcionándote confianza y un portafolio para comenzar una carrera como analista de datos asociado o junior. También sentarás las bases para otras disciplinas de datos, como la ciencia de los datos o la ingeniería de datos.

Arabic; Vietnamese; Korean; Turkish; English; Persian

"Una imagen vale mas que mil palabras". Todos estamos familiarizados con esta expresión. Se aplica especialmente cuando se trata de explicar la información obtenida del análisis de conjuntos de datos cada vez más grandes. La visualización de datos juega un papel esencial en la representación de datos tanto a pequeña como a gran escala.\n\nUna de las habilidades clave de un científico de datos es la capacidad de contar una historia convincente, visualizando datos y hallazgos de una manera accesible y estimulante. Aprender a aprovechar una herramienta de software para visualizar datos también le permitirá extraer información, comprender mejor los datos y tomar decisiones más eficaces.\n\nEl objetivo principal de este curso de Visualización de datos con Python es enseñarle cómo tomar datos que a primera vista tienen poco significado y presentarlos en una forma que tenga sentido para las personas. Se han desarrollado varias técnicas para presentar datos visualmente, pero en este curso utilizaremos varias bibliotecas de visualización de datos en Python, a saber, Matplotlib, Seaborn y Folium.\n\nOFERTA POR TIEMPO LIMITADO: La suscripción cuesta solo $ 39 USD por mes para acceder a materiales calificados y un certificado.

Scatter Plot; Map; Bar Chart; Python Programming; Pie Chart; Visualization Library; Superimposition; Bubble Chart; Choropleth Map; Box Plot

Analista de Datos de IBM

Obtén las habilidades necesarias para un puesto de analista de datos de nivel inicial mediante este Certificado Profesional de IBM, que consta de ocho cursos, y consigue una posición competitiva en el próspero mercado laboral de los analistas de datos, que experimentará un crecimiento del 20% hasta 2028 (Oficina de Estadísticas Laborales de EE.UU.)\n\nImpulsa tu carrera como analista de datos aprendiendo los principios fundamentales de la analítica de datos y adquiriendo conocimientos prácticos. Trabajarás con diversas fuentes de datos, escenarios de proyectos y herramientas de análisis de datos, incluyendo Excel, SQL, Python, Jupyter Notebooks y Cognos Analytics.\n\nEste Certificado Profesional no requiere ningún conocimiento previo de programación o estadística, y es adecuado para estudiantes con o sin títulos universitarios. Todo lo que necesitas para empezar es un conocimiento básico de informática, matemáticas de secundaria, sentirte cómodo trabajando con números, voluntad de aprender y el deseo de enriquecer tu perfil con valiosas habilidades.\n\nUna vez completado con éxito este programa, habrás analizado conjuntos de datos del mundo real, creado cuadros de mando interactivos y presentado informes para compartir tus hallazgos, proporcionándote confianza y un portafolio para comenzar una carrera como analista de datos asociado o junior. También sentarás las bases para otras disciplinas de datos, como la ciencia de los datos o la ingeniería de datos.

Vietnamese; English; Persian

En este curso se aplicarán diversas habilidades y técnicas de Data Analytics que ha aprendido como parte de los cursos anteriores del Certificado Profesional de IBM Data Analyst. Asumirá el papel de Analista de datos asociado que se ha incorporado recientemente a la organización y se enfrentará a un reto empresarial que requiere que el análisis de datos se realice en conjuntos de datos del mundo real.\n\nSe encargará de las tareas de recoger datos de múltiples fuentes, realizar análisis de datos exploratorios, la preparación y discusión de datos, el análisis estadístico y la extracción de datos, la creación de gráficos y diagramas para visualizar los datos, y la construcción de un tablero interactivo. El proyecto culminará con la presentación de su informe de análisis de datos, con un resumen ejecutivo para los diversos interesados en la organización. Se evaluará tanto su trabajo en las diversas etapas del proceso de análisis de datos como el producto final. \n\nEste proyecto es una gran oportunidad para mostrar sus habilidades de análisis de datos, y demostrar su competencia a los posibles empleadores.

Analista de Datos de IBM

Obtén las habilidades necesarias para un puesto de analista de datos de nivel inicial mediante este Certificado Profesional de IBM, que consta de ocho cursos, y consigue una posición competitiva en el próspero mercado laboral de los analistas de datos, que experimentará un crecimiento del 20% hasta 2028 (Oficina de Estadísticas Laborales de EE.UU.)\n\nImpulsa tu carrera como analista de datos aprendiendo los principios fundamentales de la analítica de datos y adquiriendo conocimientos prácticos. Trabajarás con diversas fuentes de datos, escenarios de proyectos y herramientas de análisis de datos, incluyendo Excel, SQL, Python, Jupyter Notebooks y Cognos Analytics.\n\nEste Certificado Profesional no requiere ningún conocimiento previo de programación o estadística, y es adecuado para estudiantes con o sin títulos universitarios. Todo lo que necesitas para empezar es un conocimiento básico de informática, matemáticas de secundaria, sentirte cómodo trabajando con números, voluntad de aprender y el deseo de enriquecer tu perfil con valiosas habilidades.\n\nUna vez completado con éxito este programa, habrás analizado conjuntos de datos del mundo real, creado cuadros de mando interactivos y presentado informes para compartir tus hallazgos, proporcionándote confianza y un portafolio para comenzar una carrera como analista de datos asociado o junior. También sentarás las bases para otras disciplinas de datos, como la ciencia de los datos o la ingeniería de datos.

University of Minnesota

Welcome to Introduction to Predictive Modeling, the first course in the University of Minnesota’s Analytics for Decision Making specialization.\n\nThis course will introduce to you the concepts, processes, and applications of predictive modeling, with a focus on linear regression and time series forecasting models and their practical use in Microsoft Excel. By the end of the course, you will be able to:\n\n- Understand the concepts, processes, and applications of predictive modeling.\n- Understand the structure of and intuition behind linear regression models.\n- Be able to fit simple and multiple linear regression models to data, interpret the results, evaluate the goodness of fit, and use fitted models to make predictions.\n- Understand the problem of overfitting and underfitting and be able to conduct simple model selection.\n- Understand the concepts, processes, and applications of time series forecasting as a special type of predictive modeling.\n- Be able to fit several time-series-forecasting models (e.g., exponential smoothing and Holt-Winter’s method) in Excel, evaluate the goodness of fit, and use fitted models to make forecasts.\n- Understand different types of data and how they may be used in predictive models.\n- Use Excel to prepare data for predictive modeling, including exploring data patterns, transforming data, and dealing with missing values.\n\nThis is an introductory course to predictive modeling. The course provides a combination of conceptual and hands-on learning. During the course, we will provide you opportunities to practice predictive modeling techniques on real-world datasets using Excel.\n\nTo succeed in this course, you should know basic math (the concept of functions, variables, and basic math notations such as summation and indices) and basic statistics (correlation, sample mean, standard deviation, and variance). This course does not require a background in programming, but you should be familiar with basic Excel operations (e.g., basic formulas and charting). For the best experience, you should have a recent version of Microsoft Excel installed on your computer (e.g., Excel 2013, 2016, 2019, or Office 365).

Data Analysis; Time Series Analysis; Time Series; Time Series Forecasting; Predictive Modelling; Mathematical Optimization; Modeling; Microsoft Excel; Economic Forecasting; Linear Programming

Analytics for Decision Making

The field of analytics is typically built on four pillars: Descriptive Analytics, Predictive Analytics, Causal Analytics, and Prescriptive Analytics. Descriptive analytics (e.g., visualization, BI) deal with the exploration of data for patterns, predictive analytics (e.g., data mining, time-series forecasting) identifies what can happen next, causal modeling establishes causation, and prescriptive analytics help with formulating decisions. This specialization focuses on the Prescriptive Analytics (the final pillar). This specialization will review basic predictive modeling techniques that can be used to estimate values of relevant parameters, and then use optimization and simulation techniques to formulate decisions based on these parameter values and situational constraints. The specialization will teach how to model and solve decision-making problems using predictive models, linear optimization, and simulation methods.

University of Minnesota

In this data-driven world, companies are often interested in knowing what is the "best" course of action, given the data. For example, manufacturers need to decide how many units of a product to produce given the estimated demand and raw material availability? Should they make all the products in-house or buy some from a third-party to meet the demand? Prescriptive Analytics is the branch of analytics that can provide answers to these questions. It is used for prescribing data-based decisions. The most important method in the prescriptive analytics toolbox is optimization. This course will introduce students to the basic principles of linear optimization for decision-making. Using practical examples, this course teaches how to convert a problem scenario into a mathematical model that can be solved to get the best business outcome. We will learn to identify decision variables, objective function, and constraints of a problem, and use them to formulate and solve an optimization problem using Excel solver and spreadsheet.

Mathematical Optimization; Convex Optimization; Decision Making; Microsoft Excel; Business Analytics; Data Analysis; Mathematical Model

Analytics for Decision Making

The field of analytics is typically built on four pillars: Descriptive Analytics, Predictive Analytics, Causal Analytics, and Prescriptive Analytics. Descriptive analytics (e.g., visualization, BI) deal with the exploration of data for patterns, predictive analytics (e.g., data mining, time-series forecasting) identifies what can happen next, causal modeling establishes causation, and prescriptive analytics help with formulating decisions. This specialization focuses on the Prescriptive Analytics (the final pillar). This specialization will review basic predictive modeling techniques that can be used to estimate values of relevant parameters, and then use optimization and simulation techniques to formulate decisions based on these parameter values and situational constraints. The specialization will teach how to model and solve decision-making problems using predictive models, linear optimization, and simulation methods.

University of Minnesota

Business analysts need to be able to prescribe optimal solution to problems. But analytics courses are often focused on training students in data analysis and visualization, not so much in helping them figure out how to take the available data and pair that with the right mathematical model to formulate a solution. This course is designed to connect data and models to real world decision-making scenarios in manufacturing, supply chain, finance, human resource management, etc. In particular, we understand how linear optimization - a prescriptive analytics method - can be used to formulate decision problems and provide data-based optimal solutions. Throughout this course we will work on applied problems in different industries, such as:\n\n(a) Finance Decisions: How should an investment manager create an optimal portfolio that maximizes net returns while not taking too much risks across various investments?\n\n(b) Production Decisions: Given projected demand, supply of raw materials, and transportation costs, what would be the optimal volume of products to manufacture at different plant locations?\n\n(c) HR Decisions: How many workers need to be hired or terminated over a planning horizon to minimize cost while meeting operational needs of a company?\n\n(c) Manufacturing: What would be the profit maximizing product mix that should be produced, given the raw material availability and customer demand?\n\nWe will learn how to formulate these problems as mathematical models and solve them using Excel spreadsheet.

Microsoft Excel; Mathematical Optimization; Data Analysis; Decision Making

Analytics for Decision Making

The field of analytics is typically built on four pillars: Descriptive Analytics, Predictive Analytics, Causal Analytics, and Prescriptive Analytics. Descriptive analytics (e.g., visualization, BI) deal with the exploration of data for patterns, predictive analytics (e.g., data mining, time-series forecasting) identifies what can happen next, causal modeling establishes causation, and prescriptive analytics help with formulating decisions. This specialization focuses on the Prescriptive Analytics (the final pillar). This specialization will review basic predictive modeling techniques that can be used to estimate values of relevant parameters, and then use optimization and simulation techniques to formulate decisions based on these parameter values and situational constraints. The specialization will teach how to model and solve decision-making problems using predictive models, linear optimization, and simulation methods.

University of Minnesota

This course is primarily aimed at third- and fourth-year undergraduate students or graduate students interested in learning simulation techniques to solve business problems. \n\nThe course will introduce you to take everyday and complex business problems that have no one correct answer due to uncertainties that exist in business environments. Simulation modeling allows us to explore various outcomes and protect personal or business interests against unwanted outcomes. We can model uncertainties by using the concepts of probability and stepwise thinking. Stepwise thinking allows us to break down the problem in smaller components, explore dependencies between related events and allows us to focus on aspects of problem that are prone to changes due to future uncertainties.\n\nThe course will introduce you to advanced Excel techniques to model and execute simulation models. Many of the Excel techniques learned in the course will be useful beyond simulation modeling. We will learn both Monte Carlo simulation techniques where overall outcome is of primary interest and discrete event simulation where intermediate dependencies between related events might be of interest. The course will introduce you to several practical issues in simulation modeling that are normally not covered in textbooks. The course uses a few running examples throughout the course to demonstrate concepts and provide concrete modeling examples. \n\nAfter taking the course a student will be able to develop fairly advanced simulation models to explore fairly broad range of business environments and outcomes.

Monte Carlo Method; Simulation; Data Analysis; Markov Chain Monte Carlo; Markov Model; Microsoft Excel; Mathematical Optimization; Data Clustering Algorithms; Decision Making; Simula

Analytics for Decision Making

The field of analytics is typically built on four pillars: Descriptive Analytics, Predictive Analytics, Causal Analytics, and Prescriptive Analytics. Descriptive analytics (e.g., visualization, BI) deal with the exploration of data for patterns, predictive analytics (e.g., data mining, time-series forecasting) identifies what can happen next, causal modeling establishes causation, and prescriptive analytics help with formulating decisions. This specialization focuses on the Prescriptive Analytics (the final pillar). This specialization will review basic predictive modeling techniques that can be used to estimate values of relevant parameters, and then use optimization and simulation techniques to formulate decisions based on these parameter values and situational constraints. The specialization will teach how to model and solve decision-making problems using predictive models, linear optimization, and simulation methods.

Kickstart your learning of Python for data science, as well as programming in general, with this beginner-friendly introduction to Python. Python is one of the world’s most popular programming languages, and there has never been greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries. \n\nThis course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and create a final project to demonstrate your new skills. \n\nBy the end of this course, you’ll feel comfortable creating basic programs, working with data, and solving real-world problems in Python. You’ll gain a strong foundation for more advanced learning in the field, and develop skills to help advance your career. \n\nThis course can be applied to multiple Specialization or Professional Certificate programs. Completing this course will count towards your learning in any of the following programs: \n\nIBM Applied AI Professional Certificate \n\nApplied Data Science Specialization \n\nIBM Data Science Professional Certificate \n\nUpon completion of any of the above programs, in addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing your expertise in the field.

Python Programming; Computer Programming; Numpy; Python Libraries; Pandas; Syntax; Python Syntax And Semantics; Semantics; Analysis; Data Analysis

Applied Data Science

This action-packed Specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. If you’re interested in pursuing a career in data science, and already have foundational skills or have completed the Introduction to Data Science Specialization, this program is for you!\n\nThis 4-course Specialization will give you the tools you need to analyze data and make data driven business decisions leveraging computer science and statistical analysis. You will learn Python–no prior programming knowledge necessary–and discover methods of data analysis and data visualization. You’ll utilize tools used by real data scientists like Numpy and Pandas, practice predictive modeling and model selection, and learn how to tell a compelling story with data to drive decision making.\n\nThrough guided lectures, labs, and projects in the IBM Cloud, you’ll get hands-on experience tackling interesting data problems from start to finish. Take this Specialization to solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.\n\nIn addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in applied data science.\n\nThis Specialization can also be applied toward the IBM Data Science Professional Certificate.

This mini-course is intended to for you to demonstrate foundational Python skills for working with data. The completion of this course involves working on a hands-on project where you will develop a simple dashboard using Python.\n\nThis course is part of the IBM Data Science Professional Certificate and the IBM Data Analytics Professional Certificate.\n\nPRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data.\n\nNOTE: This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.

Python Programming; Web Scraping; Web; Computer Programming; Data Analysis; Analysis; Data Manipulation; Html; Data Science; Data Structures

Applied Data Science

This action-packed Specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. If you’re interested in pursuing a career in data science, and already have foundational skills or have completed the Introduction to Data Science Specialization, this program is for you!\n\nThis 4-course Specialization will give you the tools you need to analyze data and make data driven business decisions leveraging computer science and statistical analysis. You will learn Python–no prior programming knowledge necessary–and discover methods of data analysis and data visualization. You’ll utilize tools used by real data scientists like Numpy and Pandas, practice predictive modeling and model selection, and learn how to tell a compelling story with data to drive decision making.\n\nThrough guided lectures, labs, and projects in the IBM Cloud, you’ll get hands-on experience tackling interesting data problems from start to finish. Take this Specialization to solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.\n\nIn addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in applied data science.\n\nThis Specialization can also be applied toward the IBM Data Science Professional Certificate.

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!\n\nTopics covered:\n\n1) Importing Datasets\n2) Cleaning the Data\n3) Data frame manipulation\n4) Summarizing the Data\n5) Building machine learning Regression models\n6) Building data pipelines\n\n Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts:\n\nData Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.\n\nIf you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. \n\nLIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

Analysis; Data Analysis; Python Programming; Regression; Modeling; Computer Programming; Linearity; Exploratory Data Analysis; Regression Analysis; Data Model

Applied Data Science

This action-packed Specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. If you’re interested in pursuing a career in data science, and already have foundational skills or have completed the Introduction to Data Science Specialization, this program is for you!\n\nThis 4-course Specialization will give you the tools you need to analyze data and make data driven business decisions leveraging computer science and statistical analysis. You will learn Python–no prior programming knowledge necessary–and discover methods of data analysis and data visualization. You’ll utilize tools used by real data scientists like Numpy and Pandas, practice predictive modeling and model selection, and learn how to tell a compelling story with data to drive decision making.\n\nThrough guided lectures, labs, and projects in the IBM Cloud, you’ll get hands-on experience tackling interesting data problems from start to finish. Take this Specialization to solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.\n\nIn addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in applied data science.\n\nThis Specialization can also be applied toward the IBM Data Science Professional Certificate.

French; Portuguese; Italian; Vietnamese; German; Russian; Turkish; Spanish; Arabic; Persian

"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data.\n\nOne of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions.\n\nThe main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.\n\nLIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

Data Visualization; Matplotlib; Python Programming; Map; Choropleth Map; Chart; Computer Programming; Bar Chart; Analysis; Data Analysis

Applied Data Science

This action-packed Specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. If you’re interested in pursuing a career in data science, and already have foundational skills or have completed the Introduction to Data Science Specialization, this program is for you!\n\nThis 4-course Specialization will give you the tools you need to analyze data and make data driven business decisions leveraging computer science and statistical analysis. You will learn Python–no prior programming knowledge necessary–and discover methods of data analysis and data visualization. You’ll utilize tools used by real data scientists like Numpy and Pandas, practice predictive modeling and model selection, and learn how to tell a compelling story with data to drive decision making.\n\nThrough guided lectures, labs, and projects in the IBM Cloud, you’ll get hands-on experience tackling interesting data problems from start to finish. Take this Specialization to solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.\n\nIn addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in applied data science.\n\nThis Specialization can also be applied toward the IBM Data Science Professional Certificate.

Portuguese; Italian; Vietnamese; German; Russian; Spanish; Persian; Arabic; French

This capstone project course will give you a taste of what data scientists go through in real life when working with data. \n\nYou will learn about location data and different location data providers, such as Foursquare. You will learn how to make RESTful API calls to the Foursquare API to retrieve data about venues in different neighborhoods around the world. You will also learn how to be creative in situations where data are not readily available by scraping web data and parsing HTML code. You will utilize Python and its pandas library to manipulate data, which will help you refine your skills for exploring and analyzing data. \n\nFinally, you will be required to use the Folium library to great maps of geospatial data and to communicate your results and findings.\n\nIf you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course. \n\nLIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

Data Clustering Algorithms; Analysis; Data Analysis; Python Programming; Machine Learning; Computer Programming; Data Visualization; Algorithms; Cluster Analysis; Web

Applied Data Science

This action-packed Specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. If you’re interested in pursuing a career in data science, and already have foundational skills or have completed the Introduction to Data Science Specialization, this program is for you!\n\nThis 4-course Specialization will give you the tools you need to analyze data and make data driven business decisions leveraging computer science and statistical analysis. You will learn Python–no prior programming knowledge necessary–and discover methods of data analysis and data visualization. You’ll utilize tools used by real data scientists like Numpy and Pandas, practice predictive modeling and model selection, and learn how to tell a compelling story with data to drive decision making.\n\nThrough guided lectures, labs, and projects in the IBM Cloud, you’ll get hands-on experience tackling interesting data problems from start to finish. Take this Specialization to solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.\n\nIn addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in applied data science.\n\nThis Specialization can also be applied toward the IBM Data Science Professional Certificate.

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

University of Michigan

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. \n\nThis course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

Python Programming; Computer Programming; Numpy; Analysis; Data Analysis; Data Manipulation; Pandas; General Statistics; Hypothesis; Statistical Hypothesis Testing

Applied Data Science with Python

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.\n\nIntroduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

Applied Plotting, Charting & Data Representation in Python

University of Michigan

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. \n\nThis course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.

Matplotlib; Data Visualization; Python Programming; Computer Programming; Plot (Graphics); Chart; Computer Graphics; Analysis; Data Analysis; Scatter Plot

Applied Data Science with Python

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.\n\nIntroduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

University of Michigan

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. \n\nThis course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

Machine Learning; Human Learning; Scikit-Learn; Python Programming; Algorithms; Computer Programming; Machine Learning Algorithms; Regression; Analysis; Applied Machine Learning

Applied Data Science with Python

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.\n\nIntroduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

University of Michigan

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). \n\nThis course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

Text Mining; Natural Language; Natural Language Toolkit; Language; Natural Language Processing; Python Programming; Topic Model; Regular Expression; Computer Programming; Modeling

Applied Data Science with Python

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.\n\nIntroduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

University of Michigan

This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. \n\nThis course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

Analysis; Network Analysis; Social Network; Social Network Analysis; Python Programming; Graphs; Computer Programming; Graph Theory; Network Theory; Machine Learning

Applied Data Science with Python

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.\n\nIntroduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

Introduction to R Programming for Data Science

When working in the data science field you will definitely become acquainted with the R language and the role it plays in data analysis. This course introduces you to the basics of the R language such as data types, techniques for manipulation, and how to implement fundamental programming tasks. \n\nYou will begin the process of understanding common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language. \n\nThe emphasis in this course is hands-on and practical learning . You will write a simple program using RStudio, manipulate data in a data frame or matrix, and complete a final project as a data analyst using Watson Studio and Jupyter notebooks to acquire and analyze data-driven insights. \n \n\nNo prior knowledge of R, or programming is required.

R Programming; Computer Programming; Data Analysis; SQL; Microsoft Excel; Regular Expression; Html; Framing; Parsing; Euler'S Totient Function

Applied Data Science with R

This Specialization is intended for anyone with a passion for learning who is seeking to develop the job-ready skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist.\n\nThrough these five online courses, you will develop the skills you need to bring together often disparate and disconnected data sources and use the R programming language to transform data into insights that help you and your stakeholders make more informed decisions.\n\nBy the end of this Specialization, you will be able to perform basic R programming tasks to complete the data analysis process, including data preparation, statistical analysis, and predictive modeling. You will also be able to create relational databases and query the data using SQL and R and communicate your data findings using data visualization techniques.

Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist.\n\nThe purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL and R languages. It is also intended to get you started with performing SQL access in a data science environment. \n\nThe emphasis in this course is on hands-on and practical learning . As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and R.\n\nNo prior knowledge of databases, SQL, R, or programming is required.\n\nAnyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course.

Data Analysis; Table (Database); SQL; Relational Database; Database Servers; Databases; Database Design; Ibm Db2; Metadata; R Programming

Applied Data Science with R

This Specialization is intended for anyone with a passion for learning who is seeking to develop the job-ready skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist.\n\nThrough these five online courses, you will develop the skills you need to bring together often disparate and disconnected data sources and use the R programming language to transform data into insights that help you and your stakeholders make more informed decisions.\n\nBy the end of this Specialization, you will be able to perform basic R programming tasks to complete the data analysis process, including data preparation, statistical analysis, and predictive modeling. You will also be able to create relational databases and query the data using SQL and R and communicate your data findings using data visualization techniques.

The R programming language is purpose-built for data analysis. R is the key that opens the door between the problems that you want to solve with data and the answers you need to meet your objectives. This course starts with a question and then walks you through the process of answering it through data. You will first learn important techniques for preparing (or wrangling) your data for analysis. You will then learn how to gain a better understanding of your data through exploratory data analysis, helping you to summarize your data and identify relevant relationships between variables that can lead to insights. Once your data is ready to analyze, you will learn how to develop your model and evaluate and tune its performance. By following this process, you can be sure that your data analysis performs to the standards that you have set, and you can have confidence in the results. \n\nYou will build hands-on experience by playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays. Using an Airline Reporting Carrier On-Time Performance Dataset, you will practice reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best model. \n\nWatch the videos, work through the labs, and add to your portfolio. Good luck!\n\nNote: The pre-requisite for this course is basic R programming skills. For example, ensure that you have completed a course like Introduction to R Programming for Data Science from IBM.

R Programming; Data Analysis; SQL; Microsoft Excel; Polynomial Regression; Hyperparameter Optimization; Heat Map; Categorical Variable; Extract, Transform, Load; Hyperparameter

Applied Data Science with R

This Specialization is intended for anyone with a passion for learning who is seeking to develop the job-ready skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist.\n\nThrough these five online courses, you will develop the skills you need to bring together often disparate and disconnected data sources and use the R programming language to transform data into insights that help you and your stakeholders make more informed decisions.\n\nBy the end of this Specialization, you will be able to perform basic R programming tasks to complete the data analysis process, including data preparation, statistical analysis, and predictive modeling. You will also be able to create relational databases and query the data using SQL and R and communicate your data findings using data visualization techniques.

In this course, you will learn the Grammar of Graphics, a system for describing and building graphs, and how the ggplot2 data visualization package for R applies this concept to basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots. You will also learn how to further customize your charts and plots using themes and other techniques. You will then learn how to use another data visualization package for R called Leaflet to create map plots, a unique way to plot data based on geolocation data. Finally, you will be introduced to creating interactive dashboards using the R Shiny package. You will learn how to create and customize Shiny apps, alter the appearance of the apps by adding HTML and image components, and deploy your interactive data apps on the web.\n\nYou will practice what you learn and build hands-on experience by completing labs in each module and a final project at the end of the course.\n\nWatch the videos, work through the labs, and watch your data science skill grow. Good luck!\n\nNOTE: This course requires knowledge of working with R and data. If you do not have these skills, it is highly recommended that you first take the Introduction to R Programming for Data Science as well as the Data Analysis with R courses from IBM prior to starting this course. Note: The pre-requisite for this course is basic R programming skills.

SQL; Microsoft Excel; R Programming; Data Analysis; Risk Management; Data Science; Databases

Applied Data Science with R

This Specialization is intended for anyone with a passion for learning who is seeking to develop the job-ready skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist.\n\nThrough these five online courses, you will develop the skills you need to bring together often disparate and disconnected data sources and use the R programming language to transform data into insights that help you and your stakeholders make more informed decisions.\n\nBy the end of this Specialization, you will be able to perform basic R programming tasks to complete the data analysis process, including data preparation, statistical analysis, and predictive modeling. You will also be able to create relational databases and query the data using SQL and R and communicate your data findings using data visualization techniques.

In this capstone course, you will apply various data science skills and techniques that you have learned as part of the previous courses in the IBM Data Science with R Specialization or IBM Data Analytics with Excel and R Professional Certificate.\n\nFor this project, you will assume the role of a Data Scientist who has recently joined an organization and be presented with a challenge that requires data collection, analysis, basic hypothesis testing, visualization, and modeling to be performed on real-world datasets. You will collect and understand data from multiple sources, conduct data wrangling and preparation with Tidyverse, perform exploratory data analysis with SQL, Tidyverse and ggplot2, model data with linear regression, create charts and plots to visualize the data, and build an interactive dashboard.\n\nThe project will culminate with a presentation of your data analysis report, with an executive summary for the various stakeholders in the organization.

SQL; Microsoft Excel; Data Analysis; R Programming; Risk Management

Applied Data Science with R

This Specialization is intended for anyone with a passion for learning who is seeking to develop the job-ready skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist.\n\nThrough these five online courses, you will develop the skills you need to bring together often disparate and disconnected data sources and use the R programming language to transform data into insights that help you and your stakeholders make more informed decisions.\n\nBy the end of this Specialization, you will be able to perform basic R programming tasks to complete the data analysis process, including data preparation, statistical analysis, and predictive modeling. You will also be able to create relational databases and query the data using SQL and R and communicate your data findings using data visualization techniques.

En este curso aprenderá qué es la Inteligencia Artificial (IA), explorará casos de uso y aplicaciones de IA, comprenderá conceptos y términos de IA como aprendizaje automático, aprendizaje profundo y redes neuronales. Estará expuesto a varios problemas y preocupaciones relacionados con la IA, como la ética y el sesgo, y los trabajos, y recibirá consejos de expertos sobre cómo aprender y comenzar una carrera en IA. También demostrará AI en acción con un mini proyecto.\n \nEste curso no requiere ninguna experiencia en programación o ciencias de la computación y está diseñado para presentar los conceptos básicos de inteligencia artificial a cualquier persona, ya sea que tenga una formación técnica o no.\n\nEsta es una traducción al español de un curso que se creó originalmente en inglés. Muchos de los componentes del curso se han traducido al español, incluidos títulos de lecciones, transcripciones de videos, lecturas, instrucciones de laboratorio y cuestionarios. Sin embargo, algunos componentes del curso, incluidos los videos originales y su narración, todavía están en inglés.

Artificial Neural Networks; Computer Programming; Python Programming; Machine Learning; Deep Learning; Vista; Supervision; Gustave Le Bon

Bases de Inteligencia Artificial para Todos

La Inteligencia Artificial (IA) ya no es ciencia ficción. Está impregnando rápidamente todas las industrias y tiene un impacto profundo en prácticamente todos los aspectos de nuestra existencia. Ya sea que sea un ejecutivo, un líder, un profesional de la industria, un investigador o un estudiante - comprender lo que es la IA, su impacto y potencial transformador para su organización y nuestra sociedad es de suma importancia.\n\nEsta especialización está diseñada para aquellos con poca o ninguna experiencia en IA, ya sea que tenga experiencia en tecnología o no, y no requiere ninguna habilidad en programación. Está diseñada para brindarle una comprensión firme de lo que es la IA, sus aplicaciones y casos de uso en varias industrias. Se familiarizará con términos como Aprendizaje Automático, Aprendizaje Profundo y Redes Neuronales.\n\nAdemás, lo familiarizará con los servicios de IA de IBM Watson que permiten a cualquier empresa emplear rápida y fácilmente inteligencia de IA pre construida en sus productos y soluciones. También aprenderá sobre la creación de asistentes virtuales inteligentes y cómo se pueden aprovechar en diferentes escenarios.\n\nAl final de esta especialización, los estudiantes habrán tenido interacciones prácticas con varios entornos y aplicaciones de IA, y habrán construido e implementado un chatbot habilitado para IA en un sitio web – sin ningún tipo de codificación.

En este curso aprenderá cómo comenzar rápida y fácilmente con la Inteligencia Artificial utilizando IBM Watson. Comprenderá cómo funciona Watson, se familiarizará con sus casos de uso y ejemplos de clientes de la vida real, y se le presentarán varios de los servicios de inteligencia artificial de Watson de IBM que permiten a cualquiera aplicar fácilmente la inteligencia artificial y crear aplicaciones inteligentes. También trabajará con varios servicios de Watson para demostrar la IA en acción.\n \nEste curso no requiere ninguna experiencia en programación o ciencias de la computación y está diseñado para cualquier persona, ya sea que tenga una formación técnica o no.\n\nEsta es una traducción al español de un curso que se creó originalmente en inglés. Muchos de los componentes del curso se han traducido al español, incluidos títulos de lecciones, transcripciones de videos, lecturas, instrucciones de laboratorio y cuestionarios. Sin embargo, algunos componentes del curso, incluidos los videos originales y su narración, todavía están en inglés.

Deep Learning; Gustave Le Bon; Denominación De Origen

Bases de Inteligencia Artificial para Todos

La Inteligencia Artificial (IA) ya no es ciencia ficción. Está impregnando rápidamente todas las industrias y tiene un impacto profundo en prácticamente todos los aspectos de nuestra existencia. Ya sea que sea un ejecutivo, un líder, un profesional de la industria, un investigador o un estudiante - comprender lo que es la IA, su impacto y potencial transformador para su organización y nuestra sociedad es de suma importancia.\n\nEsta especialización está diseñada para aquellos con poca o ninguna experiencia en IA, ya sea que tenga experiencia en tecnología o no, y no requiere ninguna habilidad en programación. Está diseñada para brindarle una comprensión firme de lo que es la IA, sus aplicaciones y casos de uso en varias industrias. Se familiarizará con términos como Aprendizaje Automático, Aprendizaje Profundo y Redes Neuronales.\n\nAdemás, lo familiarizará con los servicios de IA de IBM Watson que permiten a cualquier empresa emplear rápida y fácilmente inteligencia de IA pre construida en sus productos y soluciones. También aprenderá sobre la creación de asistentes virtuales inteligentes y cómo se pueden aprovechar en diferentes escenarios.\n\nAl final de esta especialización, los estudiantes habrán tenido interacciones prácticas con varios entornos y aplicaciones de IA, y habrán construido e implementado un chatbot habilitado para IA en un sitio web – sin ningún tipo de codificación.

En este curso aprenderá cómo comenzar rápida y fácilmente con la Inteligencia Artificial utilizando IBM Watson. Comprenderá cómo funciona Watson, se familiarizará con sus casos de uso y ejemplos de clientes de la vida real, y se le presentarán varios de los servicios de inteligencia artificial de Watson de IBM que permiten a cualquiera aplicar fácilmente la inteligencia artificial y crear aplicaciones inteligentes. También trabajará con varios servicios de Watson para demostrar la IA en acción.\n \nEste curso no requiere ninguna experiencia en programación o ciencias de la computación y está diseñado para cualquier persona, ya sea que tenga una formación técnica o no.\n\nEsta es una traducción al español de un curso que se creó originalmente en inglés. Muchos de los componentes del curso se han traducido al español, incluidos títulos de lecciones, transcripciones de videos, lecturas, instrucciones de laboratorio y cuestionarios. Sin embargo, algunos componentes del curso, incluidos los videos originales y su narración, todavía están en inglés.

Big Data; Python Programming; Computer Programming; Computer Program; Data Science; Joie De Vivre; Denominación De Origen; Edward De Bono; Cabeza; Gustave Le Bon

Bases de Inteligencia Artificial para Todos

La Inteligencia Artificial (IA) ya no es ciencia ficción. Está impregnando rápidamente todas las industrias y tiene un impacto profundo en prácticamente todos los aspectos de nuestra existencia. Ya sea que sea un ejecutivo, un líder, un profesional de la industria, un investigador o un estudiante - comprender lo que es la IA, su impacto y potencial transformador para su organización y nuestra sociedad es de suma importancia.\n\nEsta especialización está diseñada para aquellos con poca o ninguna experiencia en IA, ya sea que tenga experiencia en tecnología o no, y no requiere ninguna habilidad en programación. Está diseñada para brindarle una comprensión firme de lo que es la IA, sus aplicaciones y casos de uso en varias industrias. Se familiarizará con términos como Aprendizaje Automático, Aprendizaje Profundo y Redes Neuronales.\n\nAdemás, lo familiarizará con los servicios de IA de IBM Watson que permiten a cualquier empresa emplear rápida y fácilmente inteligencia de IA pre construida en sus productos y soluciones. También aprenderá sobre la creación de asistentes virtuales inteligentes y cómo se pueden aprovechar en diferentes escenarios.\n\nAl final de esta especialización, los estudiantes habrán tenido interacciones prácticas con varios entornos y aplicaciones de IA, y habrán construido e implementado un chatbot habilitado para IA en un sitio web – sin ningún tipo de codificación.

Korean; English; German

Creación de aplicaciones de IA con las API de Watson

Este curso te enseñará cómo crear chatbots útiles sin la necesidad de escribir ningún código.\n\nAprovechando las capacidades de procesamiento de lenguaje natural de IBM Watson, aprenderá a planificar, implementar, probar e implementar chatbots que deleitan a sus usuarios, en lugar de frustrarlos.\n\nFiel a nuestra promesa de no requerir ningún código, aprenderá a crear visualmente chatbots con Watson Assistant (anteriormente Watson Conversation) y cómo implementarlos en su propio sitio web a través de un práctico complemento de WordPress. ¿No tienes un sitio web? No se preocupe, se le proporcionará uno.\n\nLos chatbots son un tema candente en nuestra industria y están a punto de crecer. Todos los días se agregan nuevos trabajos que requieren esta habilidad específica, los consultores exigen tarifas premium y el interés en los chatbots está explotando rápidamente.\n\nGartner predice que para 2020, el 85% de las interacciones de los clientes con la empresa se realizará a través de medios automatizados (es decir, chatbots y tecnologías relacionadas).\n\nEsta es su oportunidad de aprender este conjunto de habilidades altamente demandadas con una introducción suave al tema que no deja piedra sin remover.\n\nEsta es una traducción al español de un curso que se creó originalmente en inglés. Muchos de los componentes del curso se han traducido al español, incluidos títulos de lecciones, transcripciones de videos, lecturas, instrucciones de laboratorio y cuestionarios. Sin embargo, algunos componentes del curso, incluidos los videos originales y su narración, todavía están en inglés.

Edward De Bono; Cabeza; Gustave Le Bon; Denominación De Origen; Joie De Vivre

Bases de Inteligencia Artificial para Todos

La Inteligencia Artificial (IA) ya no es ciencia ficción. Está impregnando rápidamente todas las industrias y tiene un impacto profundo en prácticamente todos los aspectos de nuestra existencia. Ya sea que sea un ejecutivo, un líder, un profesional de la industria, un investigador o un estudiante - comprender lo que es la IA, su impacto y potencial transformador para su organización y nuestra sociedad es de suma importancia.\n\nEsta especialización está diseñada para aquellos con poca o ninguna experiencia en IA, ya sea que tenga experiencia en tecnología o no, y no requiere ninguna habilidad en programación. Está diseñada para brindarle una comprensión firme de lo que es la IA, sus aplicaciones y casos de uso en varias industrias. Se familiarizará con términos como Aprendizaje Automático, Aprendizaje Profundo y Redes Neuronales.\n\nAdemás, lo familiarizará con los servicios de IA de IBM Watson que permiten a cualquier empresa emplear rápida y fácilmente inteligencia de IA pre construida en sus productos y soluciones. También aprenderá sobre la creación de asistentes virtuales inteligentes y cómo se pueden aprovechar en diferentes escenarios.\n\nAl final de esta especialización, los estudiantes habrán tenido interacciones prácticas con varios entornos y aplicaciones de IA, y habrán construido e implementado un chatbot habilitado para IA en un sitio web – sin ningún tipo de codificación.

The course is devoted to the systematization of the mathematical background of the students necessary for the successful mastering of educational disciplines in the field of computer vision. The course includes sections of mathematical analysis, probability theory, linear algebra.\nAim of the course:\n• Systematization of the mathematical background\n• Preparation for the use of mathematical knowledge in the professional activities of a specialist in the field of\ncomputer vision.\nPractical Learning Outcomes expected:\n• Mastering practical skills in mathematics\n• The solution of mathematical problems that are encountered in the practical work of a specialist in the field of computer vision.

Computer Vision; Mathematics; Machine Learning; Orthogonality; Invertible Matrix; Vector Spaces; Linear Independence; Matrices; Linearity; Derivative

Basics in computer vision

This Specialization is part of HSE University Master of Computer Vision degree program. Learn more about the admission into the program here and how your Coursera work can be leveraged if accepted into the program.\n\nThis specialization is intended for a wide range of specialists who want to start getting acquainted with the direction of Computer Vision. In the frame of the specialization, students can organize their mathematical and programming skills necessary for the development of algorithms in the field of Computer vision, as well as learn how to use the OpenCV library for analyzing two-dimensional images. The OpenCV library is widely used by computer vision application developers, so students will be able to apply the skills acquired in this specialization in their real practical activities.

The course is devoted to the usage of computer vision libraries like OpenCV in 2d image processing. The course includes sections of image filtering and thresholding, edge/corner/interest point detection, local and global descriptors, video tracking.\n\nAim of the course:\n•\tLearning the main algorithms of traditional image processing\n•\tThorough understanding of benefits and limitations of traditional image processing\n\nPractical Learning Outcomes expected:\n•\tMastering programming skills of image processing with computer vision libraries

Image Processing; Opencv; Computer Vision; Digital Image Processing; Computer Languages; Noise Reduction; Operations Management; Edge Detection; Binary Image; Color Image

Basics in computer vision

This Specialization is part of HSE University Master of Computer Vision degree program. Learn more about the admission into the program here and how your Coursera work can be leveraged if accepted into the program.\n\nThis specialization is intended for a wide range of specialists who want to start getting acquainted with the direction of Computer Vision. In the frame of the specialization, students can organize their mathematical and programming skills necessary for the development of algorithms in the field of Computer vision, as well as learn how to use the OpenCV library for analyzing two-dimensional images. The OpenCV library is widely used by computer vision application developers, so students will be able to apply the skills acquired in this specialization in their real practical activities.

University of California San Diego

Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world!\n\nAt the end of this course, you will be able to:\n\n* Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. \n\n* Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting.\n\n* Get value out of Big Data by using a 5-step process to structure your analysis. \n\n* Identify what are and what are not big data problems and be able to recast big data problems as data science questions.\n\n* Provide an explanation of the architectural components and programming models used for scalable big data analysis.\n\n* Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model.\n\n* Install and run a program using Hadoop!\n\nThis course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. \n\nHardware Requirements:\n(A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. \n\nSoftware Requirements:\nThis course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.

Big Data; Apache Hadoop; Mapreduce; Apache; Cloudera; Analytics; Big Data Analytics; Analysis; Data Analysis; Big Data Products

Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.\n\n*********\n\nDo you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.

Persian; Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish; Hindi

Big Data Modeling and Management Systems

University of California San Diego

Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools. Through guided hands-on tutorials, you will become familiar with techniques using real-time and semi-structured data examples. Systems and tools discussed include: AsterixDB, HP Vertica, Impala, Neo4j, Redis, SparkSQL. This course provides techniques to extract value from existing untapped data sources and discovering new data sources.\n\nAt the end of this course, you will be able to:\n * Recognize different data elements in your own work and in everyday life problems\n * Explain why your team needs to design a Big Data Infrastructure Plan and Information System Design\n * Identify the frequent data operations required for various types of data\n * Select a data model to suit the characteristics of your data \n * Apply techniques to handle streaming data\n * Differentiate between a traditional Database Management System and a Big Data Management System\n * Appreciate why there are so many data management systems\n * Design a big data information system for an online game company\n\nThis course is for those new to data science. Completion of Intro to Big Data is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications.\n\nHardware Requirements: \n(A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. \n\nSoftware Requirements: \nThis course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.

Data Model; Big Data; Modeling; Leadership and Management; Databases; Data Management; Analytics; Graphs; Big Data Analytics; Data Structures

Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.\n\n*********\n\nDo you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.

Vietnamese; Korean; German; Russian; Turkish; Spanish; Arabic; French; Portuguese; Italian

University of California San Diego

At the end of the course, you will be able to:\n\n*Retrieve data from example database and big data management systems \n*Describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications\n*Identify when a big data problem needs data integration\n*Execute simple big data integration and processing on Hadoop and Spark platforms\n\nThis course is for those new to data science. Completion of Intro to Big Data is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications.\n\nHardware Requirements: \n(A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. \n\nSoftware Requirements: \nThis course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.

Mongodb; Apache Spark; Apache; Big Data; Data Integration; PostgreSQL; SQL; Data Analysis; Analysis; Analytics

Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.\n\n*********\n\nDo you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.

Russian; Spanish; Arabic; French; Portuguese; Italian; Vietnamese; Korean; German

University of California San Diego

Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.\n\nAt the end of the course, you will be able to:\n•\tDesign an approach to leverage data using the steps in the machine learning process.\n•\tApply machine learning techniques to explore and prepare data for modeling.\n•\tIdentify the type of machine learning problem in order to apply the appropriate set of techniques.\n•\tConstruct models that learn from data using widely available open source tools.\n•\tAnalyze big data problems using scalable machine learning algorithms on Spark.\n\nSoftware Requirements: \nCloudera VM, KNIME, Spark

Machine Learning; Human Learning; Knime; Algorithms; Apache; Apache Spark; Data Clustering Algorithms; Analysis; Statistical Classification; Cluster Analysis

Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.\n\n*********\n\nDo you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.

Spanish; Arabic; French; Portuguese; Chinese; Italian; Vietnamese; Korean; German; Russian

University of California San Diego

Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data.\n\nAfter completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.

Graphs; Neo4j; Analytics; Graph Theory; Graph Database; Databases; Big Data; Apache; Apache Spark; Centrality

Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.\n\n*********\n\nDo you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.

Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

University of California San Diego

Welcome to the Capstone Project for Big Data! In this culminating project, you will build a big data ecosystem using tools and methods form the earlier courses in this specialization. You will analyze a data set simulating big data generated from a large number of users who are playing our imaginary game "Catch the Pink Flamingo". During the five week Capstone Project, you will walk through the typical big data science steps for acquiring, exploring, preparing, analyzing, and reporting. In the first two weeks, we will introduce you to the data set and guide you through some exploratory analysis using tools such as Splunk and Open Office. Then we will move into more challenging big data problems requiring the more advanced tools you have learned including KNIME, Spark's MLLib and Gephi. Finally, during the fifth and final week, we will show you how to bring it all together to create engaging and compelling reports and slide presentations. As a result of our collaboration with Splunk, a software company focus on analyzing machine-generated big data, learners with the top projects will be eligible to present to Splunk and meet Splunk recruiters and engineering leadership.

Big Data; Knime; Neo4j; Big Data Analytics; Analytics; Apache; Apache Spark; Analysis; Statistical Classification; Data Clustering Algorithms

Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.\n\n*********\n\nDo you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.

Polish; Arabic; French; Portuguese; Italian; Vietnamese; Korean; German; Russian; Spanish

Big Data: el impacto de los datos masivos en la sociedad actual

Universitat Autònoma de Barcelona

La digitalización, la informática e Internet han producido lo que se puede denominar una revolución en la acumulación y utilización de datos. Podemos almacenar y conservar más datos que nunca antes en la historia. Podemos estudiarlos y analizarlos para tomar decisiones y mejorar procesos. Esta nueva capacidad tiene un enorme impacto en todos los ámbitos de la vida social.\n\nA lo largo de este curso:\n\n•\tConoceremos qué es el Big Data y cuáles son sus características fundamentales\n•\tExploraremos el crecimiento continuo de datos, analizaremos el impacto potencial en muchos campos de la actividad humana y nos preguntaremos por los retos y desafíos que suponen en todos los órdenes de la vida social. \n•\tConoceremos las características de cada una de las fases del procesamiento Big Data, adquiriendo un lenguaje adecuado para la descripción de los procesos. Dispondremos así de una visión de conjunto sobre sistema de tratamiento de grandes datos en la actualidad.\n•\tConoceremos las principales áreas de aplicación de los datos masivos. Qué tipos de transformaciones están imponiendo en la organización del trabajo y en la gestión. Qué desafíos imponen en la gobernanza, la economía y el trabajo. Qué mejoras introducen y qué riesgos representan.\n•\tEstudiaremos las principales tecnologías e infraestructuras para el almacenamiento y procesado de grandes volúmenes de datos.

Big Data; Analytics; Big Data Analytics; Conceptualization (Information Science); Apache Hadoop; Analysis; Data Analysis; Apache; Cloud Computing; Software

Big Data – Introducción al uso práctico de datos masivos

Este programa está pensado como una entrada al mundo de los datos masivos y su tratamiento. El primer curso tiene como objetivo mostrar al estudiante el impacto del Big Data en la sociedad actual, tanto en el mundo de los negocios como en el de la política y administraciones públicas, los medios de comunicación y/o la investigación científica. A lo largo de los cursos 2, 3 y 4 se estudian la identificación, captura, pre-procesamiento, análisis y visualización de datos, desde un punto de vista “usuario”, y con una orientación práctica. Finalmente, el Capstone Project permite al estudiante aplicar los conocimientos adquiridos a un caso práctico del campo de la astronomía.\n\nAl finalizar los cursos de esta especialización el estudiante será capaz de:\n\n1. Entender el impacto del tratamiento de datos masivos en la sociedad actual.\n\n2. Entender y explicar la procedencia y características de los datos masivos.\n\n3. Adquirir, preparar, almacenar, analizar, visualizar y manejar grandes conjuntos de datos.\n\n4. Extraer información de los datos.\n\n5. Trabajar dentro del ecosistema Hadoop.\n\n6. Contestar a una pregunta bien formulada en función de la información disponible.\n\nContamos con un conjunto maravilloso de profesores, con una gran experiencia en el tema, provenientes tanto de la universidad como de la empresa.\n\nNecesitarás una computadora de 64bits que permita virtualizacion, con un mínimo de 6G de RAM (8G recomendable) y 20G disponibles en disco.

Big Data: adquisición y almacenamiento de datos

Universitat Autònoma de Barcelona

¿Estás interesado en tener un conocimiento más detallado sobre las herramientas y aplicaciones Big Data?\n\nEn este curso aprenderás los principios para comprender la terminología, conceptos básicos y herramientas más importantes para resolver problemas de análisis de datos enfocándonos en los problemas y las aplicaciones. El objetivo es proporcionar una visión de sistema para entender los retos más importantes que nos encontramos cuando trabajamos en entornos con grandes volúmenes de datos.\n\nEn el curso se plantea una introducción a diversas herramientas utilizadas de forma común en la comunidad como Hadoop, Spark o Hive y tendrás que resolver diferentes retos de análisis de datos mediante su uso.\n\nAl terminar el curso habrás adquirido conocimientos sobre el ecosistema de herramientas Big Data incluyendo ejemplos de uso con problemas industriales y científicos. Tendrás una serie de recursos sobre cómo un análisis a realizar se traduce en una serie de operaciones de recolección de datos, monitorización, almacenamiento, análisis y creación de informes sobre los resultados obtenidos. También adquirirás un criterio para elegir cuál es la herramienta más adecuada para resolver un cierto problema de análisis de datos a partir de los requerimientos de uso de las herramientas. \n\nEl curso está orientado tanto a estudiantes universitarios de primeros cursos de estudios universitarios relacionados con la informática, la ingeniería o las matemáticas, como a otros estudiantes con conocimientos de programación, interesados en aprender cómo utilizar de análisis de datos con herramientas de código abierto. Para realizar los ejercicios es necesario utilizar una máquina virtual que deberá ser instalada en tu ordenador.

Apache; Hive; Apache Spark; Apache Hadoop; Apache Hive; Big Data; Analytics; Apache Hbase; Big Data Analytics; SQL

Big Data – Introducción al uso práctico de datos masivos

Este programa está pensado como una entrada al mundo de los datos masivos y su tratamiento. El primer curso tiene como objetivo mostrar al estudiante el impacto del Big Data en la sociedad actual, tanto en el mundo de los negocios como en el de la política y administraciones públicas, los medios de comunicación y/o la investigación científica. A lo largo de los cursos 2, 3 y 4 se estudian la identificación, captura, pre-procesamiento, análisis y visualización de datos, desde un punto de vista “usuario”, y con una orientación práctica. Finalmente, el Capstone Project permite al estudiante aplicar los conocimientos adquiridos a un caso práctico del campo de la astronomía.\n\nAl finalizar los cursos de esta especialización el estudiante será capaz de:\n\n1. Entender el impacto del tratamiento de datos masivos en la sociedad actual.\n\n2. Entender y explicar la procedencia y características de los datos masivos.\n\n3. Adquirir, preparar, almacenar, analizar, visualizar y manejar grandes conjuntos de datos.\n\n4. Extraer información de los datos.\n\n5. Trabajar dentro del ecosistema Hadoop.\n\n6. Contestar a una pregunta bien formulada en función de la información disponible.\n\nContamos con un conjunto maravilloso de profesores, con una gran experiencia en el tema, provenientes tanto de la universidad como de la empresa.\n\nNecesitarás una computadora de 64bits que permita virtualizacion, con un mínimo de 6G de RAM (8G recomendable) y 20G disponibles en disco.

Universitat Autònoma de Barcelona

El presente curso tiene como objetivo presentar los métodos y técnicas básicos para el procesamiento y análisis de datos en el contexto de Big Data. No prentende ser un curso exhaustivo sobre Machine Learning ni sobre métodos Estadísticos, simplemente se pretenden mostrar las características principales de estas técnicas para que el alumno pueda tener una visión general de las opciones que ofrece el análisis de datos para poder explorar, confirmar indicios y en definitiva, extraer conclusiones.\n\nEl curso está dirigido a estudiantes y profesionales que deseen aproximarse al procesamiento y análisis de datos en Big Data. Aunque no es un requisito indispensable tener experiencia en análisis de datos o en entornos Big Data, el curso puede resultar especialmente interesante a estudiantes con ciertos conocimientos de análisis de datos que deseen introducirse en el entorno Big Data, por otro lado, también resultará interesante a aquellos estudiantes con cierta experiencia en entornos Big Data que deseen adquirir una mayor visión analítica. \n\nEn este sentido el curso pretende ofrecer recursos realistas en el contexto Big Data y por este motivo se trabajará des de una máquina virtual con la aplicación Jupyter como enlace para desarrollar los modelos y técnicas con PySpark.\n\nEl curso está dividido en 4 módulos más o menos independientes aunque se recomienda realizarlos de forma secuencial. \n\nEn el Módulo 1 se presentan los diferentes problemas y técnicas más habitules para analizar datos desde una perspectiva general. También se introduce el caso de estudio y las herramientas de trabajo que se emplearán. El resto de módulo está dedicado a la tarea de Exploración y Pre-Proceso de los datos, incluyendo consultas, tareas de gestión, resúmenes numéricos y gráficos. Los siguientes módulos se focalizan en las técnicas de análisis.\n\nEl Módulo 2 se centra en técnicas de modelización básicas, en particular regresión y regresión logística. Además de repasar las etapas de calibración del modelo, también se incluyen las etapas de validación y simplificación.\n\nEl módulo 3 está plenamente dedicado a la técnica de Árboles de Regresión y Clasificación. También se incluyen los bosques aleatorios. \n\nEl módulo final contiene la técnica de Redes Neuronales para clasificación y también una introducción a las técnicas No Supervisadas, en particular, reducción de dimensión a través del análisis de componentes principales y la clasificación automática a través del análisis de clústers.

Big Data; Big Data Analytics; Analytics; Apache Spark; Apache; Python Programming; Linear Regression; Linearity; Machine Learning; Statistical Classification

Big Data – Introducción al uso práctico de datos masivos

Este programa está pensado como una entrada al mundo de los datos masivos y su tratamiento. El primer curso tiene como objetivo mostrar al estudiante el impacto del Big Data en la sociedad actual, tanto en el mundo de los negocios como en el de la política y administraciones públicas, los medios de comunicación y/o la investigación científica. A lo largo de los cursos 2, 3 y 4 se estudian la identificación, captura, pre-procesamiento, análisis y visualización de datos, desde un punto de vista “usuario”, y con una orientación práctica. Finalmente, el Capstone Project permite al estudiante aplicar los conocimientos adquiridos a un caso práctico del campo de la astronomía.\n\nAl finalizar los cursos de esta especialización el estudiante será capaz de:\n\n1. Entender el impacto del tratamiento de datos masivos en la sociedad actual.\n\n2. Entender y explicar la procedencia y características de los datos masivos.\n\n3. Adquirir, preparar, almacenar, analizar, visualizar y manejar grandes conjuntos de datos.\n\n4. Extraer información de los datos.\n\n5. Trabajar dentro del ecosistema Hadoop.\n\n6. Contestar a una pregunta bien formulada en función de la información disponible.\n\nContamos con un conjunto maravilloso de profesores, con una gran experiencia en el tema, provenientes tanto de la universidad como de la empresa.\n\nNecesitarás una computadora de 64bits que permita virtualizacion, con un mínimo de 6G de RAM (8G recomendable) y 20G disponibles en disco.

Universitat Autònoma de Barcelona

“Visualización de datos” es el cuarto curso de la especialización “Biga Data- Uso práctico de datos masivos. Organizado en cuatro semanas, tiene por objetivo motivar e introducir los conceptos clave de la visualización de datos así como mostrar ejemplos en diferentes contextos. Además, se proporcionan criterios para formular el problema y elegir las herramientas más adecuadas para obtener una correcta visualización. Este debe ser un curso introductorio, motivador e inspirador para la narración de historias a través de la visualización de sus datos.\n\nLos cuatro módulos en los que se estructura el curso son los siguientes:\nMÓDULO 1: Contexto para la visualización de datos hoy\nMÓDULO 2: Herramientas de análisis y visualización de datos\nMÓDULO 3: El proceso de creación de una visualización de datos\nMÓDULO 4: Otros aspectos de la visualización de datos

Big Data; Tableau Software; Analytics; Yottabyte; Big Data Analytics; Software Visualization; Data Visualization; Audit; Software; Denominación De Origen

Big Data – Introducción al uso práctico de datos masivos

Este programa está pensado como una entrada al mundo de los datos masivos y su tratamiento. El primer curso tiene como objetivo mostrar al estudiante el impacto del Big Data en la sociedad actual, tanto en el mundo de los negocios como en el de la política y administraciones públicas, los medios de comunicación y/o la investigación científica. A lo largo de los cursos 2, 3 y 4 se estudian la identificación, captura, pre-procesamiento, análisis y visualización de datos, desde un punto de vista “usuario”, y con una orientación práctica. Finalmente, el Capstone Project permite al estudiante aplicar los conocimientos adquiridos a un caso práctico del campo de la astronomía.\n\nAl finalizar los cursos de esta especialización el estudiante será capaz de:\n\n1. Entender el impacto del tratamiento de datos masivos en la sociedad actual.\n\n2. Entender y explicar la procedencia y características de los datos masivos.\n\n3. Adquirir, preparar, almacenar, analizar, visualizar y manejar grandes conjuntos de datos.\n\n4. Extraer información de los datos.\n\n5. Trabajar dentro del ecosistema Hadoop.\n\n6. Contestar a una pregunta bien formulada en función de la información disponible.\n\nContamos con un conjunto maravilloso de profesores, con una gran experiencia en el tema, provenientes tanto de la universidad como de la empresa.\n\nNecesitarás una computadora de 64bits que permita virtualizacion, con un mínimo de 6G de RAM (8G recomendable) y 20G disponibles en disco.

Universitat Autònoma de Barcelona

En este último curso de la Especialización Big Data el estudiante tendrá la oportunidad de aplicar algunas de las herramientas y métodos aprendidos en los cursos anteriores en un caso práctico.\n\nEl objetivo de este Capstone Project es mostrar un ejemplo del trabajo que se realiza diariamente en el departamento de Cosmología del Port d’Informació Científica, en Barcelona. Se trata de crear un clasificador para imágenes de galaxias, a partir de datos del proyecto GalaxyZoo e imágenes y datos del telescopio Sloan Digital Sky Survey. Los trabajos y ejercicios guiados llevarán al estudiante a la exploración y analisis de estos datos, hasta realizar una herramienta automática de Machine Learning.\n\nEl proceso seguido por los estudiantes en este curso se podría aplicar en cualquier otra disciplina, por ejemplo en las ciencias sociales, en un estudio de mercado o en cualquier ámbito que comporte toma de decisiones a partir de un gran volumen de datos.

Big Data; Apache Hbase; Big Data Analytics; Apache Hadoop; Machine Learning; Analytics; Apache; Apache Spark; Raw Image Format; Denominación De Origen

Big Data – Introducción al uso práctico de datos masivos

Este programa está pensado como una entrada al mundo de los datos masivos y su tratamiento. El primer curso tiene como objetivo mostrar al estudiante el impacto del Big Data en la sociedad actual, tanto en el mundo de los negocios como en el de la política y administraciones públicas, los medios de comunicación y/o la investigación científica. A lo largo de los cursos 2, 3 y 4 se estudian la identificación, captura, pre-procesamiento, análisis y visualización de datos, desde un punto de vista “usuario”, y con una orientación práctica. Finalmente, el Capstone Project permite al estudiante aplicar los conocimientos adquiridos a un caso práctico del campo de la astronomía.\n\nAl finalizar los cursos de esta especialización el estudiante será capaz de:\n\n1. Entender el impacto del tratamiento de datos masivos en la sociedad actual.\n\n2. Entender y explicar la procedencia y características de los datos masivos.\n\n3. Adquirir, preparar, almacenar, analizar, visualizar y manejar grandes conjuntos de datos.\n\n4. Extraer información de los datos.\n\n5. Trabajar dentro del ecosistema Hadoop.\n\n6. Contestar a una pregunta bien formulada en función de la información disponible.\n\nContamos con un conjunto maravilloso de profesores, con una gran experiencia en el tema, provenientes tanto de la universidad como de la empresa.\n\nNecesitarás una computadora de 64bits que permita virtualizacion, con un mínimo de 6G de RAM (8G recomendable) y 20G disponibles en disco.

The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This is an introductory course in the use of Excel and is designed to give you a working knowledge of Excel with the aim of getting to use it for more advance topics in Business Statistics later. The course is designed keeping in mind two kinds of learners - those who have very little functional knowledge of Excel and those who use Excel regularly but at a peripheral level and wish to enhance their skills. The course takes you from basic operations such as reading data into excel using various data formats, organizing and manipulating data, to some of the more advanced functionality of Excel. All along, Excel functionality is introduced using easy to understand examples which are demonstrated in a way that learners can become comfortable in understanding and applying them.\n\nTo successfully complete course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later. \n________________________________________\nWEEK 1\nModule 1: Introduction to Spreadsheets\nIn this module, you will be introduced to the use of Excel spreadsheets and various basic data functions of Excel.\n\nTopics covered include:\n•\tReading data into Excel using various formats\n•\tBasic functions in Excel, arithmetic as well as various logical functions\n•\tFormatting rows and columns\n•\tUsing formulas in Excel and their copy and paste using absolute and relative referencing\n________________________________________\nWEEK 2\nModule 2: Spreadsheet Functions to Organize Data\nThis module introduces various Excel functions to organize and query data. Learners are introduced to the IF, nested IF, VLOOKUP and the HLOOKUP functions of Excel. \n\nTopics covered include:\n•\tIF and the nested IF functions\n•\tVLOOKUP and HLOOKUP\n•\tThe RANDBETWEEN function\n________________________________________\nWEEK 3\nModule 3: Introduction to Filtering, Pivot Tables, and Charts\nThis module introduces various data filtering capabilities of Excel. You’ll learn how to set filters in data to selectively access data. A very powerful data summarizing tool, the Pivot Table, is also explained and we begin to introduce the charting feature of Excel.\n\nTopics covered include:\n•\tVLOOKUP across worksheets\n•\tData filtering in Excel\n•\tUse of Pivot tables with categorical as well as numerical data\n•\tIntroduction to the charting capability of Excel\n________________________________________\nWEEK 4\nModule 4: Advanced Graphing and Charting\nThis module explores various advanced graphing and charting techniques available in Excel. Starting with various line, bar and pie charts we introduce pivot charts, scatter plots and histograms. You will get to understand these various charts and get to build them on your own.\n\nTopics covered include\n•\tLine, Bar and Pie charts\n•\tPivot charts\n•\tScatter plots\n•\tHistograms

Microsoft Excel; Analysis; Data Analysis; Pivot Table; Chart; Histogram; Pivot Chart; Lookup Table; Data Visualization; Data Manipulation

Business Statistics and Analysis

The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. You’ll master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. You’ll also explore basic probability concepts, including measuring and modeling uncertainty, and you’ll use various data distributions, along with the Linear Regression Model, to analyze and inform business decisions. The Specialization culminates with a Capstone Project in which you’ll apply the skills and knowledge you’ve gained to an actual business problem.\n\nTo successfully complete all course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later.\n\nTo see an overview video for this Specialization, click here!

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions

The ability to understand and apply Business Statistics is becoming increasingly important in the industry. A good understanding of Business Statistics is a requirement to make correct and relevant interpretations of data. Lack of knowledge could lead to erroneous decisions which could potentially have negative consequences for a firm. This course is designed to introduce you to Business Statistics. We begin with the notion of descriptive statistics, which is summarizing data using a few numbers. Different categories of descriptive measures are introduced and discussed along with the Excel functions to calculate them. The notion of probability or uncertainty is introduced along with the concept of a sample and population data using relevant business examples. This leads us to various statistical distributions along with their Excel functions which are then used to model or approximate business processes. You get to apply these descriptive measures of data and various statistical distributions using easy-to-follow Excel based examples which are demonstrated throughout the course.\n\nTo successfully complete course assignments, students must have access to Microsoft Excel. \n________________________________________\nWEEK 1\nModule 1: Basic Data Descriptors\nIn this module you will get to understand, calculate and interpret various descriptive or summary measures of data. These descriptive measures summarize and present data using a few numbers. Appropriate Excel functions to do these calculations are introduced and demonstrated.\n\nTopics covered include:\n•\tCategories of descriptive data\n•\tMeasures of central tendency, the mean, median, mode, and their interpretations and calculations\n•\tMeasures of spread-in-data, the range, interquartile-range, standard deviation and variance\n•\tBox plots\n•\tInterpreting the standard deviation measure using the rule-of-thumb and Chebyshev’s theorem\n________________________________________\nWEEK 2\nModule 2: Descriptive Measures of Association, Probability, and Statistical Distributions\nThis module presents the covariance and correlation measures and their respective Excel functions. You get to understand the notion of causation versus correlation. The module then introduces the notion of probability and random variables and starts introducing statistical distributions.\n\nTopics covered include:\n•\tMeasures of association, the covariance and correlation measures; causation versus correlation\n•\tProbability and random variables; discrete versus continuous data\n•\tIntroduction to statistical distributions\n________________________________________\nWEEK 3\nModule 3: The Normal Distribution\nThis module introduces the Normal distribution and the Excel function to calculate probabilities and various outcomes from the distribution. \n\nTopics covered include:\n•\tProbability density function and area under the curve as a measure of probability\n•\tThe Normal distribution (bell curve), NORM.DIST, NORM.INV functions in Excel\n________________________________________\nWEEK 4\nModule 4: Working with Distributions, Normal, Binomial, Poisson\nIn this module, you'll see various applications of the Normal distribution. You will also get introduced to the Binomial and Poisson distributions. The Central Limit Theorem is introduced and explained in the context of understanding sample data versus population data and the link between the two.\n\nTopics covered include:\n•\tVarious applications of the Normal distribution\n•\tThe Binomial and Poisson distributions\n•\tSample versus population data; the Central Limit Theorem

General Statistics; Studentized Residual; Probability; Normal Distribution; Poisson Distribution; Analysis; Binomial Distribution; Chi-Squared Distribution; Statistical Analysis; Microsoft Excel

Business Statistics and Analysis

The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. You’ll master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. You’ll also explore basic probability concepts, including measuring and modeling uncertainty, and you’ll use various data distributions, along with the Linear Regression Model, to analyze and inform business decisions. The Specialization culminates with a Capstone Project in which you’ll apply the skills and knowledge you’ve gained to an actual business problem.\n\nTo successfully complete all course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later.\n\nTo see an overview video for this Specialization, click here!

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

Business Applications of Hypothesis Testing and Confidence Interval Estimation

Confidence intervals and Hypothesis tests are very important tools in the Business Statistics toolbox. A mastery over these topics will help enhance your business decision making and allow you to understand and measure the extent of ‘risk’ or ‘uncertainty’ in various business processes. \nThis is the third course in the specialization "Business Statistics and Analysis" and the course advances your knowledge about Business Statistics by introducing you to Confidence Intervals and Hypothesis Testing. We first conceptually understand these tools and their business application. We then introduce various calculations to constructing confidence intervals and to conduct different kinds of Hypothesis Tests. These are done by easy to understand applications.\n\nTo successfully complete course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later. Please note that earlier versions of Microsoft Excel (2007 and earlier) will not be compatible to some Excel functions covered in this course. \n\n\nWEEK 1\nModule 1: Confidence Interval - Introduction\nIn this module you will get to conceptually understand what a confidence interval is and how is its constructed. We will introduce the various building blocks for the confidence interval such as the t-distribution, the t-statistic, the z-statistic and their various excel formulas. We will then use these building blocks to construct confidence intervals.\n\nTopics covered include:\n•\tIntroducing the t-distribution, the T.DIST and T.INV excel functions\n•\tConceptual understanding of a Confidence Interval\n•\tThe z-statistic and the t-statistic\n•\tConstructing a Confidence Interval using z-statistic and t-statistic \n\n\nWEEK 2\nModule 2: Confidence Interval - Applications\nThis module presents various business applications of the confidence interval including an application where we use the confidence interval to calculate an appropriate sample size. We also introduce with an application, the confidence interval for a population proportion. Towards the close of module we start introducing the concept of Hypothesis Testing.\n\nTopics covered include:\n•\tApplications of Confidence Interval\n•\tConfidence Interval for a Population Proportion\n•\tSample Size Calculation\n•\tHypothesis Testing, An Introduction\n\n\nWEEK 3\nModule 3: Hypothesis Testing\nThis module introduces Hypothesis Testing. You get to understand the logic behind hypothesis tests. The four steps for conducting a hypothesis test are introduced and you get to apply them for hypothesis tests for a population mean as well as population proportion. You will understand the difference between single tail hypothesis tests and two tail hypothesis tests and also the Type I and Type II errors associated with hypothesis tests and ways to reduce such errors. \n\nTopics covered include:\n•\tThe Logic of Hypothesis Testing\n•\tThe Four Steps for conducting a Hypothesis Test\n•\tSingle Tail and Two Tail Hypothesis Tests\n•\tGuidelines, Formulas and an Application of Hypothesis Test\n•\tHypothesis Test for a Population Proportion\n•\tType I and Type II Errors in a Hypothesis \n\n\nWEEK 4\nModule 4: Hypothesis Test - Differences in Mean\nIn this module, you'll apply Hypothesis Tests to test the difference between two different data, such hypothesis tests are called difference in means tests. We will introduce the three kinds of difference in means test and apply them to various business applications. We will also introduce the Excel dialog box to conduct such hypothesis tests.\n\nTopics covered include:\n•\tIntroducing the Difference-In-Means Hypothesis Test\n•\tApplications of the Difference-In-Means Hypothesis Test\n•\tThe Equal & Unequal Variance Assumption and the Paired t-test for difference in means.\n•\tSome more applications

Statistical Hypothesis Testing; Hypothesis; Hypothesis Testing; General Statistics; Analysis; Confidence Interval; Confidence; Microsoft Excel; Data Analysis; Relative Change And Difference

Business Statistics and Analysis

The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. You’ll master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. You’ll also explore basic probability concepts, including measuring and modeling uncertainty, and you’ll use various data distributions, along with the Linear Regression Model, to analyze and inform business decisions. The Specialization culminates with a Capstone Project in which you’ll apply the skills and knowledge you’ve gained to an actual business problem.\n\nTo successfully complete all course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later.\n\nTo see an overview video for this Specialization, click here!

Vietnamese; German; Russian; Spanish; Arabic; French; Portuguese; Italian

Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction. \nThis is the fourth course in the specialization, "Business Statistics and Analysis". The course introduces you to the very important tool known as Linear Regression. You will learn to apply various procedures such as dummy variable regressions, transforming variables, and interaction effects. All these are introduced and explained using easy to understand examples in Microsoft Excel.\nThe focus of the course is on understanding and application, rather than detailed mathematical derivations.\nNote: This course uses the ‘Data Analysis’ tool box which is standard with the Windows version of Microsoft Excel. It is also standard with the 2016 or later Mac version of Excel. However, it is not standard with earlier versions of Excel for Mac. \n\n\nWEEK 1\nModule 1: Regression Analysis: An Introduction\nIn this module you will get introduced to the Linear Regression Model. We will build a regression model and estimate it using Excel. We will use the estimated model to infer relationships between various variables and use the model to make predictions. The module also introduces the notion of errors, residuals and R-square in a regression model.\n\nTopics covered include:\n•\tIntroducing the Linear Regression\n•\tBuilding a Regression Model and estimating it using Excel\n•\tMaking inferences using the estimated model\n•\tUsing the Regression model to make predictions\n•\tErrors, Residuals and R-square\n \n\nWEEK 2\nModule 2: Regression Analysis: Hypothesis Testing and Goodness of Fit\nThis module presents different hypothesis tests you could do using the Regression output. These tests are an important part of inference and the module introduces them using Excel based examples. The p-values are introduced along with goodness of fit measures R-square and the adjusted R-square. Towards the end of module we introduce the ‘Dummy variable regression’ which is used to incorporate categorical variables in a regression. \n\nTopics covered include:\n•\tHypothesis testing in a Linear Regression\n•\t‘Goodness of Fit’ measures (R-square, adjusted R-square)\n•\tDummy variable Regression (using Categorical variables in a Regression)\n \n\nWEEK 3\nModule 3: Regression Analysis: Dummy Variables, Multicollinearity\nThis module continues with the application of Dummy variable Regression. You get to understand the interpretation of Regression output in the presence of categorical variables. Examples are worked out to re-inforce various concepts introduced. The module also explains what is Multicollinearity and how to deal with it. \n\nTopics covered include:\n•\tDummy variable Regression (using Categorical variables in a Regression)\n•\tInterpretation of coefficients and p-values in the presence of Dummy variables\n•\tMulticollinearity in Regression Models\n \n\nWEEK 4\nModule 4: Regression Analysis: Various Extensions\nThe module extends your understanding of the Linear Regression, introducing techniques such as mean-centering of variables and building confidence bounds for predictions using the Regression model. A powerful regression extension known as ‘Interaction variables’ is introduced and explained using examples. We also study the transformation of variables in a regression and in that context introduce the log-log and the semi-log regression models. \n\nTopics covered include:\n•\tMean centering of variables in a Regression model\n•\tBuilding confidence bounds for predictions using a Regression model\n•\tInteraction effects in a Regression\n•\tTransformation of variables\n•\tThe log-log and semi-log regression models

Regression; Linear Regression; Linearity; Analysis; Regression Analysis; General Statistics; Multicollinearity; Interaction (Statistics); Microsoft Excel; Log Log Plot

Business Statistics and Analysis

The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. You’ll master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. You’ll also explore basic probability concepts, including measuring and modeling uncertainty, and you’ll use various data distributions, along with the Linear Regression Model, to analyze and inform business decisions. The Specialization culminates with a Capstone Project in which you’ll apply the skills and knowledge you’ve gained to an actual business problem.\n\nTo successfully complete all course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later.\n\nTo see an overview video for this Specialization, click here!

Spanish; Arabic; French; Portuguese; Italian; Vietnamese; German; Russian

The Business Statistics and Analysis Capstone is an opportunity to apply various skills developed across the four courses in the specialization to a real life data. The Capstone, in collaboration with an industry partner uses publicly available ‘Housing Data’ to pose various questions typically a client would pose to a data analyst.\nYour job is to do the relevant statistical analysis and report your findings in response to the questions in a way that anyone can understand.\nPlease remember that this is a Capstone, and has a degree of difficulty/ambiguity higher than the previous four courses. The aim being to mimic a real life application as close as possible.

Analysis; General Statistics; Regression; Microsoft Excel; Data Analysis; Regression Analysis; Statistical Analysis; Analytics; Housing; Business Analytics

Business Statistics and Analysis

The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. You’ll master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. You’ll also explore basic probability concepts, including measuring and modeling uncertainty, and you’ll use various data distributions, along with the Linear Regression Model, to analyze and inform business decisions. The Specialization culminates with a Capstone Project in which you’ll apply the skills and knowledge you’ve gained to an actual business problem.\n\nTo successfully complete all course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later.\n\nTo see an overview video for this Specialization, click here!

Russian; Spanish; Arabic; French; Portuguese; Italian; Vietnamese; German

Indian School of Business

The explosion in digital media - web, social and now mobile - represents a departure from how things were like in the last century. This proliferation of digital media is both a threat and an opportunity for many businesses. Business Analytics can be leveraged to process data, sentiment, buzz, contacts, context and other aspects of business interest in real time, for business performance and impact. The course picks and uses use-cases from a variety of industries and geographies, to showcase the potential and impact that business analytics done properly (or not) can have on business performance.

Analytics; Business Analytics; Digital Media; Analysis; Cluster Analysis; Secondary Data; Factorization; Factor Analysis; Perceptual Mapping; Augmented Assignment

Business Technology Management

With digital transformation, products and business models in today's competitive environment are increasingly being transformed by technology. This new digital economy places information technology (IT) at the centre of firm strategy and operations, and requires a new breed of IT managers and leaders who can examine technology through a business lens.\n\nThe Business Technology Management Specialization will empower you with knowledge of the IT domain, project management, leadership and team building skills, and functional and analytical skills. These skills are critical skills for leveraging technology to create competitive advantage.\n\nThis Specialization will introduce you to the IT-powered digital transformation that business are going through. It will enable you to understand how insightful executives leverage IT to create value and understand the competitive dynamics of industries that consume significant technology.\n\nIt will also introduce you to Business Analytics in a world of digital media, including the right toolscape, like being able to run the Analytics Desktop with leading tools like RStudio and GitHub.\n\nFinally, this Specialization will introduce you to customer analytics and enable you to develop an appreciation of problem-solving, data collection, prediction and optimization that can be enabled using digital media tools.\n\nVideo: Professor Deepa Mani speaks about the Specialization

Arabic; French; Portuguese; Italian; Vietnamese; German; Russian; Spanish

Solve Business Problems with AI and Machine Learning

Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services.\n\nThis is the first of four courses in the Certified Artificial Intelligence Practitioner (CAIP) professional certification. This course is meant as an entry point into the world of AI/ML. You'll learn about the business problems that AI/ML can solve, as well as the specific AI/ML technologies that can solve them. In addition, you'll get an overview of the general workflow involved in machine learning, as well as the tools and other resources that support it. This course also promotes the importance of ethics in AI/ML, and provides you with techniques for addressing ethical challenges.\n\nUltimately, this course will get you thinking about the "why?" of AI/ML, and it will ensure that your more technical work in later courses is done with clear business goals in mind.

Machine Learning; Project; Risk; Evaluation; Information Privacy; Quantum Computing; Operations Management; Planning; Modeling; Strategy

CertNexus Certified Artificial Intelligence Practitioner

The Certified Artificial Intelligence Practitioner™ (CAIP) industry validated certification helps professionals draw higher salaries (25% on average) and differentiate themselves from other job candidates.\n\nArtificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This specialization shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.\n\nThe specialization is designed for data science practitioners entering the field of artificial intelligence and will prepare learners for the CAIP certification exam.\n\nYour journey to CAIP Certification\n\n1) Complete the Coursera Certified Artificial Intelligence Practitioner Professional Certificate\n\n2) Review the CAIP AIP Exam Blueprint\n\n3) Purchase your CAIP Exam Voucher\n\n4) Register for your CAIP Exam

Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that practitioners must follow from beginning to end. It is this process—also called a workflow—that enables the organization to get the most useful results out of their machine learning technologies. No matter what form the final product or service takes, leveraging the workflow is key to the success of the business's AI solution. \n\nThis second course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate explores each step along the machine learning workflow, from problem formulation all the way to model presentation and deployment. The overall workflow was introduced in the previous course, but now you'll take a deeper dive into each of the important tasks that make up the workflow, including two of the most hands-on tasks: data analysis and model training. You'll also learn about how machine learning tasks can be automated, ensuring that the workflow can recur as needed, like most important business processes.\n\nUltimately, this course provides a practical framework upon which you'll build many more machine learning models in the remaining courses.

Machine Learning; Histogram; General Statistics; Map; Noise; Scatter Plot; Test Set; Finalization; Business Solutions; Measurement

CertNexus Certified Artificial Intelligence Practitioner

The Certified Artificial Intelligence Practitioner™ (CAIP) industry validated certification helps professionals draw higher salaries (25% on average) and differentiate themselves from other job candidates.\n\nArtificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This specialization shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.\n\nThe specialization is designed for data science practitioners entering the field of artificial intelligence and will prepare learners for the CAIP certification exam.\n\nYour journey to CAIP Certification\n\n1) Complete the Coursera Certified Artificial Intelligence Practitioner Professional Certificate\n\n2) Review the CAIP AIP Exam Blueprint\n\n3) Purchase your CAIP Exam Voucher\n\n4) Register for your CAIP Exam

Build Regression, Classification, and Clustering Models

In most cases, the ultimate goal of a machine learning project is to produce a model. Models make decisions, predictions—anything that can help the business understand itself, its customers, and its environment better than a human could. Models are constructed using algorithms, and in the world of machine learning, there are many different algorithms to choose from. You need to know how to select the best algorithm for a given job, and how to use that algorithm to produce a working model that provides value to the business.\n\nThis third course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate introduces you to some of the major machine learning algorithms that are used to solve the two most common supervised problems: regression and classification, and one of the most common unsupervised problems: clustering. You'll build multiple models to address each of these problems using the machine learning workflow you learned about in the previous course.\n\nUltimately, this course begins a technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.

Regression; Statistical Classification; Data Clustering Algorithms; Linear Regression; Linear Algebra; Unsupervised Learning; K-Means Clustering; Sigma-Algebra; Regression Analysis; Linearity

CertNexus Certified Artificial Intelligence Practitioner

The Certified Artificial Intelligence Practitioner™ (CAIP) industry validated certification helps professionals draw higher salaries (25% on average) and differentiate themselves from other job candidates.\n\nArtificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This specialization shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.\n\nThe specialization is designed for data science practitioners entering the field of artificial intelligence and will prepare learners for the CAIP certification exam.\n\nYour journey to CAIP Certification\n\n1) Complete the Coursera Certified Artificial Intelligence Practitioner Professional Certificate\n\n2) Review the CAIP AIP Exam Blueprint\n\n3) Purchase your CAIP Exam Voucher\n\n4) Register for your CAIP Exam

Build Decision Trees, SVMs, and Artificial Neural Networks

There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more. Adding all of these algorithms to your skillset is crucial for selecting the best tool for the job.\n\nThis fourth and final course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate continues on from the previous course by introducing more, and in some cases, more advanced algorithms used in both machine learning and deep learning. As before, you'll build multiple models that can solve business problems, and you'll do so within a workflow.\n\nUltimately, this course concludes the technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.

Decision Tree; Artificial Neural Networks; Perceptron; Multilayer Perceptron; Decision Tree Model; Recurrent Neural Network; Random Forest; Randomness; Natural Language Processing; Microsoft Excel

CertNexus Certified Artificial Intelligence Practitioner

The Certified Artificial Intelligence Practitioner™ (CAIP) industry validated certification helps professionals draw higher salaries (25% on average) and differentiate themselves from other job candidates.\n\nArtificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This specialization shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.\n\nThe specialization is designed for data science practitioners entering the field of artificial intelligence and will prepare learners for the CAIP certification exam.\n\nYour journey to CAIP Certification\n\n1) Complete the Coursera Certified Artificial Intelligence Practitioner Professional Certificate\n\n2) Review the CAIP AIP Exam Blueprint\n\n3) Purchase your CAIP Exam Voucher\n\n4) Register for your CAIP Exam

Promote the Ethical Use of Data-Driven Technologies

The greatest risk in emerging technology is the perpetuation of bias in automated technologies dependent upon data sets. Solutions created with racial, gender or demographic bias, whether unintentional or not can perpetuate tragic inequities socially and economically. This is the first of five courses within the Certified Ethical Emerging Technologist (CEET) professional certificate and it is designed for learners seeking to advocate and promote the ethical use of data-driven technologies. Students will learn what emerging technologies are and how they can be used to create data driven solutions. You will learn types of bias and common ethical theories and how they can be applied to emerging technology, and examine legal and ethical privacy concepts as they relate to technologies such as artificial intelligence, machine learning and data science fields. Throughout the course learners begin to distinguish which types of bias may cause the greatest risk and which principles to apply to strategically respond to ethical considerations.

Ethics; Bias; Privacy; Determinism; Criticism; Evaluation; Machine Learning; Relative Change And Difference; Legal Solutions; Virtue

CertNexus Certified Ethical Emerging Technologist

The Certified Ethical Emerging Technologist (CEET) industry validated certification helps professionals draw higher salaries (25% on average) and differentiate themselves from other job candidates.\n\nOrganizations and governments are seeking out ethics professionals to minimize risk and guide their decision-making about the design of inclusive, responsible, and trusted technology. An algorithm not designed and assessed in alignment with ethical standards can create further inequity across race, gender and marginalized populations. The reputational and financial impact of an ethics violation can devastate a company. Knowledgeable ethics leaders are needed who can navigate through the more than 160 frameworks and guidelines to select and implement the best strategy to promote fairness and minimize risk for their organization. This specialization is designed for learners who want to create and lead initiatives that prioritize ethical integrity within emerging data-driven technology fields such as artificial intelligence and data science and will be prepared to bridge the gap between theory and practice.\n\nYour journey to CEET Certification\n\n1) Complete the Coursera Certified Ethical Emerging Technologist Professional Certificate\n\n2) Review the CEET CET-110 Exam Blueprint\n\n3) Purchase your CEET Exam Voucher\n\n4) Register for your CEET Exam

Arabic; French; Russian; Spanish; Portuguese; Italian; Vietnamese; German

Turn Ethical Frameworks into Actionable Steps

Ethical principles build a strong foundation for driving ethical technologies. Principles alone can be elusive and impractical for application. Ethical frameworks based upon these principles provide a structure to guide technologists when implementing data-driven solutions. However, ethical frameworks, along with standards and regulations, can make compliance tasks more complex, and they can also raise the tension between ethical duties and business practicalities. An approach is needed to reconcile these issues. This second course within the Certified Ethical Emerging Technologist (CEET) professional certificate is designed for learners seeking to analyze ethical frameworks, regulations, standards, and best practices and integrate them into data-driven solutions.\n\nStudents will become familiar with frameworks and the common ethical principles they are based upon and how they can be applied across a variety of ethically driven dilemmas. You will learn applicable regulations and best practices established across global organizations and governments and how to navigate the integration of these standards in the context of business needs.\n\nThis course is the second of five courses within the Certified Ethical Emerging Technologist (CEET) professional certificate. The preceding course is titled Promote the Ethical Use of Data-Driven Technologies.

Ethics; Project Management; Leadership and Management; Project; Machine Ethics; Industry Self-Regulation; Risk-Based Auditing; Competence-Based Management; ATLAS.ti; Analytic And Enumerative Statistical Studies

CertNexus Certified Ethical Emerging Technologist

The Certified Ethical Emerging Technologist (CEET) industry validated certification helps professionals draw higher salaries (25% on average) and differentiate themselves from other job candidates.\n\nOrganizations and governments are seeking out ethics professionals to minimize risk and guide their decision-making about the design of inclusive, responsible, and trusted technology. An algorithm not designed and assessed in alignment with ethical standards can create further inequity across race, gender and marginalized populations. The reputational and financial impact of an ethics violation can devastate a company. Knowledgeable ethics leaders are needed who can navigate through the more than 160 frameworks and guidelines to select and implement the best strategy to promote fairness and minimize risk for their organization. This specialization is designed for learners who want to create and lead initiatives that prioritize ethical integrity within emerging data-driven technology fields such as artificial intelligence and data science and will be prepared to bridge the gap between theory and practice.\n\nYour journey to CEET Certification\n\n1) Complete the Coursera Certified Ethical Emerging Technologist Professional Certificate\n\n2) Review the CEET CET-110 Exam Blueprint\n\n3) Purchase your CEET Exam Voucher\n\n4) Register for your CEET Exam

French; Portuguese; Russian; Spanish

Data-driven technologies like AI, when designed with ethics in mind, benefit both the business and society at large. But it’s not enough to say you will “be ethical” and expect it to happen. We need tools and techniques to help us assess gaps in our ethical behaviors and to identify and stop threats to our ethical goals. We also need to know where and how to improve our ethical processes across development lifecycles. What we need is a way to manage ethical risk. This third course in the Certified Ethical Emerging Technologist (CEET) professional certificate is designed for learners seeking to detect and mitigate ethical risks in the design, development, and deployment of data-driven technologies. Students will learn the fundamentals of ethical risk analysis, sources of risk, and how to manage different types of risk. Throughout the course, learners will learn strategies for identifying and mitigating risks.\n\nThis course is the third of five courses within the Certified Ethical Emerging Technologist (CEET) professional certificate. The preceding courses are titled Promote the Ethical Use of Data-Driven Technologies and Turn Ethical Frameworks into Actionable Steps.

Ethics; Risk-Based Auditing; Competence-Based Management; Dynamic Decision-Making; Interactive Visual Analysis; Team Performance Management; Dbase; Knowledge Integration; Alphaic; Knowledge-Based Recommender System

CertNexus Certified Ethical Emerging Technologist

The Certified Ethical Emerging Technologist (CEET) industry validated certification helps professionals draw higher salaries (25% on average) and differentiate themselves from other job candidates.\n\nOrganizations and governments are seeking out ethics professionals to minimize risk and guide their decision-making about the design of inclusive, responsible, and trusted technology. An algorithm not designed and assessed in alignment with ethical standards can create further inequity across race, gender and marginalized populations. The reputational and financial impact of an ethics violation can devastate a company. Knowledgeable ethics leaders are needed who can navigate through the more than 160 frameworks and guidelines to select and implement the best strategy to promote fairness and minimize risk for their organization. This specialization is designed for learners who want to create and lead initiatives that prioritize ethical integrity within emerging data-driven technology fields such as artificial intelligence and data science and will be prepared to bridge the gap between theory and practice.\n\nYour journey to CEET Certification\n\n1) Complete the Coursera Certified Ethical Emerging Technologist Professional Certificate\n\n2) Review the CEET CET-110 Exam Blueprint\n\n3) Purchase your CEET Exam Voucher\n\n4) Register for your CEET Exam

French; Portuguese; Russian; Spanish

Communicate Effectively about Ethical Challenges in Data-Driven Technologies

Leading a data-driven organization necessitates effective communication to create a culture of ethical practice. Communication to stakeholders will guide an organization's strategy and potentially impact the future of work for that organization or entity. It is not enough to talk about ethical practices, you need to to relate their value to stakeholders. Building out strategies that are inclusive and relatable can build public trust and loyalty, and knowing how to plan for a crisis will reduce the harm to such trust and loyalty. \n\nIn this fourth course of the CertNexus Certified Ethical Emerging Technologist (CEET) professional certificate, learners will develop inclusive strategies to communicate business impacts to stakeholders, design communication strategies that mirror ethical principles and policies, and in case of an ethical crisis, be prepared to manage the crisis and the media to reduce business impact.\n\nThis course is the fourth of five courses within the Certified Ethical Emerging Technologist (CEET) professional certificate. The preceding courses are titled Promote the Ethical Use of Data-Driven Technologies, Turn Ethical Frameworks into Actionable Steps, and Detect and Mitigate Ethical Risks.

Ethics; Listening; Data Transmission; Public Budgeting; HR Project Management; Knowledge Entrepreneurship; Procurement Planning; Risk-Based Auditing; Competence-Based Management; Industry Self-Regulation

CertNexus Certified Ethical Emerging Technologist

The Certified Ethical Emerging Technologist (CEET) industry validated certification helps professionals draw higher salaries (25% on average) and differentiate themselves from other job candidates.\n\nOrganizations and governments are seeking out ethics professionals to minimize risk and guide their decision-making about the design of inclusive, responsible, and trusted technology. An algorithm not designed and assessed in alignment with ethical standards can create further inequity across race, gender and marginalized populations. The reputational and financial impact of an ethics violation can devastate a company. Knowledgeable ethics leaders are needed who can navigate through the more than 160 frameworks and guidelines to select and implement the best strategy to promote fairness and minimize risk for their organization. This specialization is designed for learners who want to create and lead initiatives that prioritize ethical integrity within emerging data-driven technology fields such as artificial intelligence and data science and will be prepared to bridge the gap between theory and practice.\n\nYour journey to CEET Certification\n\n1) Complete the Coursera Certified Ethical Emerging Technologist Professional Certificate\n\n2) Review the CEET CET-110 Exam Blueprint\n\n3) Purchase your CEET Exam Voucher\n\n4) Register for your CEET Exam

Create and Lead an Ethical Data-Driven Organization

Creating and leading an ethical data-driven organization, when done successfully, is a cultural transformation for an organization. Navigating a cultural shift requires leadership buy in, resourcing, training, and support through creation of boards, policies, and governance. Beyond leadership and organization, it is imperative to engage employees through forums and incentive programs for continual involvement. A strong understanding of ethical organizational policies provides the foundation for consistent monitoring to maintain an ethical culture. \n\nIn this fifth course of the CertNexus Certified Ethical Emerging Technologist (CEET) professional certificate, learners will develop strategies to lead an applied ethics initiative, champion its crucial importance, and promote an ethical organizational culture. Learners will learn how to develop and implement ethical organizational policies and a code of ethics. They will also be prepared to evaluate the effectiveness of policies with internal and external stakeholders.\n\nThis course is the fifth of five courses within the Certified Ethical Emerging Technologist (CEET) professional certificate. The preceding courses are titled Promote the Ethical Use of Data-Driven Technologies, Turn Ethical Frameworks into Actionable Steps, Detect and Mitigate Ethical Risks, and Communicate Effectively about Ethical Challenges in Data-Driven Technologies.

Data Governance; Ethics; Operational Auditing; Machine Ethics; Industry Self-Regulation; Competence-Based Management; Risk-Based Auditing; Procurement Planning; HR Project Management; Knowledge Entrepreneurship

CertNexus Certified Ethical Emerging Technologist

The Certified Ethical Emerging Technologist (CEET) industry validated certification helps professionals draw higher salaries (25% on average) and differentiate themselves from other job candidates.\n\nOrganizations and governments are seeking out ethics professionals to minimize risk and guide their decision-making about the design of inclusive, responsible, and trusted technology. An algorithm not designed and assessed in alignment with ethical standards can create further inequity across race, gender and marginalized populations. The reputational and financial impact of an ethics violation can devastate a company. Knowledgeable ethics leaders are needed who can navigate through the more than 160 frameworks and guidelines to select and implement the best strategy to promote fairness and minimize risk for their organization. This specialization is designed for learners who want to create and lead initiatives that prioritize ethical integrity within emerging data-driven technology fields such as artificial intelligence and data science and will be prepared to bridge the gap between theory and practice.\n\nYour journey to CEET Certification\n\n1) Complete the Coursera Certified Ethical Emerging Technologist Professional Certificate\n\n2) Review the CEET CET-110 Exam Blueprint\n\n3) Purchase your CEET Exam Voucher\n\n4) Register for your CEET Exam

En este curso aprenderá cómo comenzar rápida y fácilmente con la Inteligencia Artificial utilizando IBM Watson. Comprenderá cómo funciona Watson, se familiarizará con sus casos de uso y ejemplos de clientes de la vida real, y se le presentarán varios de los servicios de inteligencia artificial de Watson de IBM que permiten a cualquiera aplicar fácilmente la inteligencia artificial y crear aplicaciones inteligentes. También trabajará con varios servicios de Watson para demostrar la IA en acción.\n \nEste curso no requiere ninguna experiencia en programación o ciencias de la computación y está diseñado para cualquier persona, ya sea que tenga una formación técnica o no.\n\nEsta es una traducción al español de un curso que se creó originalmente en inglés. Muchos de los componentes del curso se han traducido al español, incluidos títulos de lecciones, transcripciones de videos, lecturas, instrucciones de laboratorio y cuestionarios. Sin embargo, algunos componentes del curso, incluidos los videos originales y su narración, todavía están en inglés.

Big Data; Python Programming; Computer Programming; Computer Program; Data Science; Joie De Vivre; Denominación De Origen; Edward De Bono; Cabeza; Gustave Le Bon

Ciencia de Datos Aplicada

Esta especialización repleta de actividades está dirigida a los entusiastas de la ciencia de datos que desean adquirir conocimientos prácticos. Si estás interesado en seguir una carrera en ciencia de datos, y ya tienes conocimientos básicos o has completado la Especialización de Introducción a la Ciencia de Datos, ¡este programa es para ti!\n\nEsta Especialización de 4 cursos te ofrecerá las herramientas necesarias para analizar datos y tomar decisiones empresariales basadas en datos, utilizando la informática y el análisis estadístico. Aprenderás Python -sin necesidad de conocimientos previos de programación- y descubrirás métodos de análisis y visualización de datos. Utilizarás herramientas que emplean los científicos de datos del mundo real, como Numpy y Pandas, practicarás el modelado predictivo y la selección de modelos.\n\nGracias a las charlas guiadas, los laboratorios y los proyectos en IBM Cloud, obtendrás una experiencia práctica para abordar interesantes problemas de datos de principio a fin. Realiza esta Especialización para consolidar tus conocimientos de Python y ciencia de datos antes de sumergirte a fondo en big data, IA y deep learning.\n\nAdemás de obtener un certificado de Coursera por realizar la Especialización, también recibirás una insignia digital de IBM que te reconocerá como un especialista en ciencia de datos aplicada.\n\nEsta Especialización también puede utilizarse para el Certificado Profesional de Ciencia de Datos de IBM.

Korean; English; German

Aprenda a analizar datos con Python. Este curso lo llevará desde los conceptos básicos de Python hasta la exploración de muchos tipos diferentes de datos. Aprenderá a preparar datos para el análisis, realizar análisis estadísticos simples, crear visualizaciones de datos significativas, predecir tendencias futuras a partir de datos, ¡y más!\n\nTópicos cubiertos:\n\n1) Importación de conjuntos de datos\n2) Limpiar los datos\n3) manipulación del marco de datos\n4) Resumen de los datos\n5) Creación de modelos de regresión de aprendizaje automático\n6) Construcción de canalizaciones de datos\n\n El análisis de datos con Python se entregará a través de conferencias, laboratorio y asignaciones. Incluye las siguientes partes:\n\nBibliotecas de análisis de datos: aprenderá a usar las bibliotecas Pandas, Numpy y Scipy para trabajar con un conjunto de datos de muestra. Le presentaremos pandas, una biblioteca de código abierto, y la usaremos para cargar, manipular, analizar y visualizar conjuntos de datos interesantes. Luego, le presentaremos otra biblioteca de código abierto, scikit-learn, y usaremos algunos de sus algoritmos de aprendizaje automático para construir modelos inteligentes y hacer predicciones interesantes.\n\nSi elige tomar este curso y obtener el certificado del curso de Coursera, también obtendrá una insignia digital de IBM.\n\nOFERTA POR TIEMPO LIMITADO: La suscripción cuesta solo $ 39 USD por mes para acceder a materiales calificados y un certificado.

Python Programming; Modeling; 2.5d; Evaluation; Computer Programming; Summary Statistics; Missing Data; Categorical Data; Pivot Table; Extract, Transform, Load

Ciencia de Datos Aplicada

Esta especialización repleta de actividades está dirigida a los entusiastas de la ciencia de datos que desean adquirir conocimientos prácticos. Si estás interesado en seguir una carrera en ciencia de datos, y ya tienes conocimientos básicos o has completado la Especialización de Introducción a la Ciencia de Datos, ¡este programa es para ti!\n\nEsta Especialización de 4 cursos te ofrecerá las herramientas necesarias para analizar datos y tomar decisiones empresariales basadas en datos, utilizando la informática y el análisis estadístico. Aprenderás Python -sin necesidad de conocimientos previos de programación- y descubrirás métodos de análisis y visualización de datos. Utilizarás herramientas que emplean los científicos de datos del mundo real, como Numpy y Pandas, practicarás el modelado predictivo y la selección de modelos.\n\nGracias a las charlas guiadas, los laboratorios y los proyectos en IBM Cloud, obtendrás una experiencia práctica para abordar interesantes problemas de datos de principio a fin. Realiza esta Especialización para consolidar tus conocimientos de Python y ciencia de datos antes de sumergirte a fondo en big data, IA y deep learning.\n\nAdemás de obtener un certificado de Coursera por realizar la Especialización, también recibirás una insignia digital de IBM que te reconocerá como un especialista en ciencia de datos aplicada.\n\nEsta Especialización también puede utilizarse para el Certificado Profesional de Ciencia de Datos de IBM.

Arabic; Vietnamese; Korean; Turkish; English; Persian

"Una imagen vale mas que mil palabras". Todos estamos familiarizados con esta expresión. Se aplica especialmente cuando se trata de explicar la información obtenida del análisis de conjuntos de datos cada vez más grandes. La visualización de datos juega un papel esencial en la representación de datos tanto a pequeña como a gran escala.\n\nUna de las habilidades clave de un científico de datos es la capacidad de contar una historia convincente, visualizando datos y hallazgos de una manera accesible y estimulante. Aprender a aprovechar una herramienta de software para visualizar datos también le permitirá extraer información, comprender mejor los datos y tomar decisiones más eficaces.\n\nEl objetivo principal de este curso de Visualización de datos con Python es enseñarle cómo tomar datos que a primera vista tienen poco significado y presentarlos en una forma que tenga sentido para las personas. Se han desarrollado varias técnicas para presentar datos visualmente, pero en este curso utilizaremos varias bibliotecas de visualización de datos en Python, a saber, Matplotlib, Seaborn y Folium.\n\nOFERTA POR TIEMPO LIMITADO: La suscripción cuesta solo $ 39 USD por mes para acceder a materiales calificados y un certificado.

Scatter Plot; Map; Bar Chart; Python Programming; Pie Chart; Visualization Library; Superimposition; Bubble Chart; Choropleth Map; Box Plot

Ciencia de Datos Aplicada

Esta especialización repleta de actividades está dirigida a los entusiastas de la ciencia de datos que desean adquirir conocimientos prácticos. Si estás interesado en seguir una carrera en ciencia de datos, y ya tienes conocimientos básicos o has completado la Especialización de Introducción a la Ciencia de Datos, ¡este programa es para ti!\n\nEsta Especialización de 4 cursos te ofrecerá las herramientas necesarias para analizar datos y tomar decisiones empresariales basadas en datos, utilizando la informática y el análisis estadístico. Aprenderás Python -sin necesidad de conocimientos previos de programación- y descubrirás métodos de análisis y visualización de datos. Utilizarás herramientas que emplean los científicos de datos del mundo real, como Numpy y Pandas, practicarás el modelado predictivo y la selección de modelos.\n\nGracias a las charlas guiadas, los laboratorios y los proyectos en IBM Cloud, obtendrás una experiencia práctica para abordar interesantes problemas de datos de principio a fin. Realiza esta Especialización para consolidar tus conocimientos de Python y ciencia de datos antes de sumergirte a fondo en big data, IA y deep learning.\n\nAdemás de obtener un certificado de Coursera por realizar la Especialización, también recibirás una insignia digital de IBM que te reconocerá como un especialista en ciencia de datos aplicada.\n\nEsta Especialización también puede utilizarse para el Certificado Profesional de Ciencia de Datos de IBM.

Vietnamese; English; Persian

Este curso de proyecto final le dará una idea de lo que atraviesan los científicos de datos en la vida real cuando trabajan con datos.\n\nAprenderá sobre datos de ubicación y diferentes proveedores de datos de ubicación, como Foursquare. Aprenderá cómo realizar llamadas de API RESTful a la API de Foursquare para recuperar datos sobre lugares en diferentes vecindarios de todo el mundo. También aprenderá a ser creativo en situaciones en las que los datos no están disponibles fácilmente al extraer datos web y analizar el código HTML. Utilizará Python y su biblioteca de pandas para manipular datos, lo que lo ayudará a refinar sus habilidades para explorar y analizar datos.\n\nFinalmente, se le pedirá que utilice la biblioteca Folium para obtener excelentes mapas de datos geoespaciales y para comunicar sus resultados y hallazgos.\n\nSi elige tomar este curso y obtener el certificado del curso de Coursera, también obtendrá una insignia digital de IBM al completar con éxito el curso.\n\nOFERTA POR TIEMPO LIMITADO: La suscripción cuesta solo $ 39 USD por mes para acceder a materiales calificados y un certificado.

Credential; K-Means Clustering; Parsing; Pandas; Yottabyte; Unsupervised Learning; Accounting; Web; Application Programming Interfaces; Data Clustering Algorithms

Ciencia de Datos Aplicada

Esta especialización repleta de actividades está dirigida a los entusiastas de la ciencia de datos que desean adquirir conocimientos prácticos. Si estás interesado en seguir una carrera en ciencia de datos, y ya tienes conocimientos básicos o has completado la Especialización de Introducción a la Ciencia de Datos, ¡este programa es para ti!\n\nEsta Especialización de 4 cursos te ofrecerá las herramientas necesarias para analizar datos y tomar decisiones empresariales basadas en datos, utilizando la informática y el análisis estadístico. Aprenderás Python -sin necesidad de conocimientos previos de programación- y descubrirás métodos de análisis y visualización de datos. Utilizarás herramientas que emplean los científicos de datos del mundo real, como Numpy y Pandas, practicarás el modelado predictivo y la selección de modelos.\n\nGracias a las charlas guiadas, los laboratorios y los proyectos en IBM Cloud, obtendrás una experiencia práctica para abordar interesantes problemas de datos de principio a fin. Realiza esta Especialización para consolidar tus conocimientos de Python y ciencia de datos antes de sumergirte a fondo en big data, IA y deep learning.\n\nAdemás de obtener un certificado de Coursera por realizar la Especialización, también recibirás una insignia digital de IBM que te reconocerá como un especialista en ciencia de datos aplicada.\n\nEsta Especialización también puede utilizarse para el Certificado Profesional de Ciencia de Datos de IBM.

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El arte de descubrir los conocimientos y las tendencias de los datos ha existido desde la antigüedad. Los antiguos egipcios usaron datos del censo para aumentar la eficiencia en la recaudación de impuestos y predijeron con precisión la inundación del río Nilo cada año. Desde entonces, las personas que trabajan en ciencia de datos han creado un campo único y distinto para el trabajo que realizan. Este campo es ciencia de datos. En este curso, conoceremos a algunos profesionales de la ciencia de datos y obtendremos una visión general de lo que es hoy la ciencia de datos.

Deliverable; Analytics; Regression; Deep Learning; Regression Analysis; Computer Vision; Persona (User Experience); Machine Learning; Exercise; Analysis

Ciencia de Datos de IBM

La ciencia de datos es una de las profesiones más populares de la década, y la demanda de científicos de datos que puedan analizar datos y comunicar resultados para informar decisiones basadas en datos nunca ha sido mayor. Este Certificado Profesional de IBM ayudará a cualquier persona interesada en seguir una carrera en ciencia de datos o aprendizaje automático a desarrollar habilidades y experiencia relevantes para su carrera.\n\nEs un mito que para convertirse en científico de datos se necesita un doctorado. Cualquier persona con pasión por el aprendizaje puede tomar este Certificado Profesional (no se requieren conocimientos previos de informática o lenguajes de programación) y desarrollar las habilidades, herramientas y portafolio para tener una ventaja competitiva en el mercado laboral como científico de datos de nivel básico.\n\nAl completar con éxito estos cursos, habrá creado una cartera de proyectos de ciencia de datos para brindarle la confianza necesaria para sumergirse en una profesión emocionante en ciencia de datos.\n\nAdemás de obtener un Certificado Profesional de Coursera, también recibirá una insignia digital de IBM que reconoce su competencia en ciencia de datos.

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¿Cuáles son algunas de las herramientas de ciencia de datos más populares, cómo las usa y cuáles son sus características? En este curso, aprenderá sobre Jupyter Notebooks, RStudio IDE, Apache Zeppelin y Data Science Experience. Aprenderá para qué se utiliza cada herramienta, qué lenguajes de programación pueden ejecutar, sus características y limitaciones. Con las herramientas alojadas en la nube en Cognitive Class Labs, podrá probar cada herramienta y seguir las instrucciones para ejecutar código simple en Python, R o Scala. Para finalizar el curso, creará un proyecto final con un Jupyter Notebook en IBM Data Science Experience y demostrará su competencia preparando un cuaderno, escribiendo Markdown y compartiendo su trabajo con sus compañeros.

Python Programming; Open Source; Application Programming Interfaces; Language; Sources; Modeling; Computer Programming; Project; Rstudio; Peering

Ciencia de Datos de IBM

La ciencia de datos es una de las profesiones más populares de la década, y la demanda de científicos de datos que puedan analizar datos y comunicar resultados para informar decisiones basadas en datos nunca ha sido mayor. Este Certificado Profesional de IBM ayudará a cualquier persona interesada en seguir una carrera en ciencia de datos o aprendizaje automático a desarrollar habilidades y experiencia relevantes para su carrera.\n\nEs un mito que para convertirse en científico de datos se necesita un doctorado. Cualquier persona con pasión por el aprendizaje puede tomar este Certificado Profesional (no se requieren conocimientos previos de informática o lenguajes de programación) y desarrollar las habilidades, herramientas y portafolio para tener una ventaja competitiva en el mercado laboral como científico de datos de nivel básico.\n\nAl completar con éxito estos cursos, habrá creado una cartera de proyectos de ciencia de datos para brindarle la confianza necesaria para sumergirse en una profesión emocionante en ciencia de datos.\n\nAdemás de obtener un Certificado Profesional de Coursera, también recibirá una insignia digital de IBM que reconoce su competencia en ciencia de datos.

A pesar del reciente aumento de la potencia informática y el acceso a los datos durante las últimas dos décadas, nuestra capacidad para utilizar los datos en el proceso de toma de decisiones se pierde o no se maximiza con demasiada frecuencia, no tenemos una comprensión sólida de las preguntas que se hacen y cómo aplicar los datos correctamente al problema en cuestión.\n\nEste curso tiene un propósito, y es compartir una metodología que se pueda utilizar dentro de la ciencia de datos, para garantizar que los datos utilizados en la resolución de problemas sean relevantes y se manipulen adecuadamente para abordar la cuestión en cuestión.\n\nEn consecuencia, en este curso aprenderá:\n - Los principales pasos necesarios para abordar un problema de ciencia de datos.\n - Los principales pasos involucrados en la práctica de la ciencia de datos, desde la formación de un negocio concreto o un problema de investigación, hasta la recopilación y análisis de datos, la construcción de un modelo y la comprensión de los comentarios después de la implementación del modelo.\n - ¡Cómo piensan los científicos de datos!\n\nOFERTA POR TIEMPO LIMITADO: La suscripción cuesta solo $ 39 USD por mes para acceder a materiales calificados y un certificado.

Methodology; Data Model; Modeling; Cleaning; Cmos; Average; Process; Flowchart; Planning; Error

Ciencia de Datos de IBM

La ciencia de datos es una de las profesiones más populares de la década, y la demanda de científicos de datos que puedan analizar datos y comunicar resultados para informar decisiones basadas en datos nunca ha sido mayor. Este Certificado Profesional de IBM ayudará a cualquier persona interesada en seguir una carrera en ciencia de datos o aprendizaje automático a desarrollar habilidades y experiencia relevantes para su carrera.\n\nEs un mito que para convertirse en científico de datos se necesita un doctorado. Cualquier persona con pasión por el aprendizaje puede tomar este Certificado Profesional (no se requieren conocimientos previos de informática o lenguajes de programación) y desarrollar las habilidades, herramientas y portafolio para tener una ventaja competitiva en el mercado laboral como científico de datos de nivel básico.\n\nAl completar con éxito estos cursos, habrá creado una cartera de proyectos de ciencia de datos para brindarle la confianza necesaria para sumergirse en una profesión emocionante en ciencia de datos.\n\nAdemás de obtener un Certificado Profesional de Coursera, también recibirá una insignia digital de IBM que reconoce su competencia en ciencia de datos.

Arabic; French; Portuguese; Italian; German; Russian; English; Persian

En este curso aprenderá cómo comenzar rápida y fácilmente con la Inteligencia Artificial utilizando IBM Watson. Comprenderá cómo funciona Watson, se familiarizará con sus casos de uso y ejemplos de clientes de la vida real, y se le presentarán varios de los servicios de inteligencia artificial de Watson de IBM que permiten a cualquiera aplicar fácilmente la inteligencia artificial y crear aplicaciones inteligentes. También trabajará con varios servicios de Watson para demostrar la IA en acción.\n \nEste curso no requiere ninguna experiencia en programación o ciencias de la computación y está diseñado para cualquier persona, ya sea que tenga una formación técnica o no.\n\nEsta es una traducción al español de un curso que se creó originalmente en inglés. Muchos de los componentes del curso se han traducido al español, incluidos títulos de lecciones, transcripciones de videos, lecturas, instrucciones de laboratorio y cuestionarios. Sin embargo, algunos componentes del curso, incluidos los videos originales y su narración, todavía están en inglés.

Big Data; Python Programming; Computer Programming; Computer Program; Data Science; Joie De Vivre; Denominación De Origen; Edward De Bono; Cabeza; Gustave Le Bon

Ciencia de Datos de IBM

La ciencia de datos es una de las profesiones más populares de la década, y la demanda de científicos de datos que puedan analizar datos y comunicar resultados para informar decisiones basadas en datos nunca ha sido mayor. Este Certificado Profesional de IBM ayudará a cualquier persona interesada en seguir una carrera en ciencia de datos o aprendizaje automático a desarrollar habilidades y experiencia relevantes para su carrera.\n\nEs un mito que para convertirse en científico de datos se necesita un doctorado. Cualquier persona con pasión por el aprendizaje puede tomar este Certificado Profesional (no se requieren conocimientos previos de informática o lenguajes de programación) y desarrollar las habilidades, herramientas y portafolio para tener una ventaja competitiva en el mercado laboral como científico de datos de nivel básico.\n\nAl completar con éxito estos cursos, habrá creado una cartera de proyectos de ciencia de datos para brindarle la confianza necesaria para sumergirse en una profesión emocionante en ciencia de datos.\n\nAdemás de obtener un Certificado Profesional de Coursera, también recibirá una insignia digital de IBM que reconoce su competencia en ciencia de datos.

Korean; English; German

Gran parte de los datos del mundo residen en bases de datos. SQL (o lenguaje de consulta estructurado) es un lenguaje poderoso que se utiliza para comunicarse y extraer datos de bases de datos. Un conocimiento práctico de bases de datos y SQL es imprescindible si desea convertirse en un científico de datos.\n\nEl propósito de este curso es presentar los conceptos de bases de datos relacionales y ayudarlo a aprender y aplicar los conocimientos básicos del lenguaje SQL. También está destinado a ayudarle a empezar a realizar el acceso SQL en un entorno de ciencia de datos.\n\nEl énfasis en este curso está en el aprendizaje práctico y práctico. Como tal, trabajará con bases de datos reales, herramientas de ciencia de datos reales y conjuntos de datos del mundo real. Creará una instancia de base de datos en la nube. A través de una serie de prácticas de laboratorio, practicará la creación y ejecución de consultas SQL. También aprenderá cómo acceder a las bases de datos desde los cuadernos de Jupyter usando SQL y Python.\n\nNo se requieren conocimientos previos de bases de datos, SQL, Python o programación.\n\nCualquiera puede auditar este curso sin cargo. Si elige tomar este curso y obtener el certificado del curso de Coursera, también puede obtener una insignia digital de IBM al completar con éxito el curso.\n\nOFERTA POR TIEMPO LIMITADO: La suscripción cuesta solo $39 USD por mes para acceder a materiales calificados y un certificado.

Databases; SQL; Relational Database; Cloud Computing; Grouped Data; Euler'S Totient Function; Sorting; Ordered Pair; Operations Management; Big Data

Ciencia de Datos de IBM

La ciencia de datos es una de las profesiones más populares de la década, y la demanda de científicos de datos que puedan analizar datos y comunicar resultados para informar decisiones basadas en datos nunca ha sido mayor. Este Certificado Profesional de IBM ayudará a cualquier persona interesada en seguir una carrera en ciencia de datos o aprendizaje automático a desarrollar habilidades y experiencia relevantes para su carrera.\n\nEs un mito que para convertirse en científico de datos se necesita un doctorado. Cualquier persona con pasión por el aprendizaje puede tomar este Certificado Profesional (no se requieren conocimientos previos de informática o lenguajes de programación) y desarrollar las habilidades, herramientas y portafolio para tener una ventaja competitiva en el mercado laboral como científico de datos de nivel básico.\n\nAl completar con éxito estos cursos, habrá creado una cartera de proyectos de ciencia de datos para brindarle la confianza necesaria para sumergirse en una profesión emocionante en ciencia de datos.\n\nAdemás de obtener un Certificado Profesional de Coursera, también recibirá una insignia digital de IBM que reconoce su competencia en ciencia de datos.

Arabic; Vietnamese; Korean; English

Aprenda a analizar datos con Python. Este curso lo llevará desde los conceptos básicos de Python hasta la exploración de muchos tipos diferentes de datos. Aprenderá a preparar datos para el análisis, realizar análisis estadísticos simples, crear visualizaciones de datos significativas, predecir tendencias futuras a partir de datos, ¡y más!\n\nTópicos cubiertos:\n\n1) Importación de conjuntos de datos\n2) Limpiar los datos\n3) manipulación del marco de datos\n4) Resumen de los datos\n5) Creación de modelos de regresión de aprendizaje automático\n6) Construcción de canalizaciones de datos\n\n El análisis de datos con Python se entregará a través de conferencias, laboratorio y asignaciones. Incluye las siguientes partes:\n\nBibliotecas de análisis de datos: aprenderá a usar las bibliotecas Pandas, Numpy y Scipy para trabajar con un conjunto de datos de muestra. Le presentaremos pandas, una biblioteca de código abierto, y la usaremos para cargar, manipular, analizar y visualizar conjuntos de datos interesantes. Luego, le presentaremos otra biblioteca de código abierto, scikit-learn, y usaremos algunos de sus algoritmos de aprendizaje automático para construir modelos inteligentes y hacer predicciones interesantes.\n\nSi elige tomar este curso y obtener el certificado del curso de Coursera, también obtendrá una insignia digital de IBM.\n\nOFERTA POR TIEMPO LIMITADO: La suscripción cuesta solo $ 39 USD por mes para acceder a materiales calificados y un certificado.

Python Programming; Modeling; 2.5d; Evaluation; Computer Programming; Summary Statistics; Missing Data; Categorical Data; Pivot Table; Extract, Transform, Load

Ciencia de Datos de IBM

La ciencia de datos es una de las profesiones más populares de la década, y la demanda de científicos de datos que puedan analizar datos y comunicar resultados para informar decisiones basadas en datos nunca ha sido mayor. Este Certificado Profesional de IBM ayudará a cualquier persona interesada en seguir una carrera en ciencia de datos o aprendizaje automático a desarrollar habilidades y experiencia relevantes para su carrera.\n\nEs un mito que para convertirse en científico de datos se necesita un doctorado. Cualquier persona con pasión por el aprendizaje puede tomar este Certificado Profesional (no se requieren conocimientos previos de informática o lenguajes de programación) y desarrollar las habilidades, herramientas y portafolio para tener una ventaja competitiva en el mercado laboral como científico de datos de nivel básico.\n\nAl completar con éxito estos cursos, habrá creado una cartera de proyectos de ciencia de datos para brindarle la confianza necesaria para sumergirse en una profesión emocionante en ciencia de datos.\n\nAdemás de obtener un Certificado Profesional de Coursera, también recibirá una insignia digital de IBM que reconoce su competencia en ciencia de datos.

Arabic; Vietnamese; Korean; Turkish; English; Persian