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 Email us: instructor.success@datacamp.comApply Below:
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TitleDescriptionContent AreaContent TypeTechnology
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Distributed AI Model Training in PythonTo train models on consumer-grade hardware, you’ll need to optimize the model training process to get the most out of your processing power. This coding course will cover how to optimize the model training process depending on the hardware available. As well as optimizing hardware, the course should cover smart architecture and training loop choices to optimize training loop memory usage and runtimes, such as gradient accumulation, local SGD, low-precision training, and others. Learners aren’t expected to know how different hardware options (e.g., GPU) enable distributed computing, so some computation fundamentals will need to be covered. Applicants are encouraged to use Hugging Face’s accelerate library in this course for accelerating PyTorch model training, but we will accept applications using other libraries with good reasons.Artificial IntelligenceCoursePythonApplication Form
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Deploying AI into Production with FastAPIDeploying models as APIs has become a popular method for making them easily accessible to systems in a developer-friendly way. This course will cover how to take models from scripts to production using FastAPI to create the API and uvicorn to deploy the server, as well as running the application from a Docker container. As the course progresses, we would like learners to add features to their API, such as authentication and usage limits to replicate many real-world use cases. Prior to the course, learners will be familiar with GET and POST operations in FastAPI.Artificial IntelligenceCoursePythonApplication Form
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Introduction to PySparkThis two- or three-chapter course will teach learners what Spark is and how to use Spark from Python! It will also introduce learners to the challenges of working with big data. Learners will learn to interactively work with DataFrames and PySpark SQL to process and manipulate large datasets. The course should replace the existing Introduction to PySpark course and be an entry point to our PySpark curriculum for data and AI engineers, data scientists, and machine learning engineers. The prerequisite will be Joining Data with pandas. Emerging CurriculumCoursePySparkApplication Form
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Advanced GitThis course explores advanced topics and techniques using Git. Learners will discover why and how to perform various (non-default) merges, including squashing and rebasing. The course will introduce concepts such as cherry-picking, bisecting, and reference logging, concluding with how to work with submodules and worktrees. Before taking the course, learners will be familiar with saving, reverting, and searching in Git, as well as working with branches and remotes.Emerging CurriculumCourseGitApplication Form
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Case Study: DatabricksThis case study will provide learners hands-on experience using Databricks to tackle real-world problems. Using a fictitious company within a specific industry (such as finance, FMCG, or healthcare), learners will take on the role of a Data Analyst. Learners will take on tasks associated with managing data, utilizing SQL in Databricks, creating visuals and dashboards, and performing analytical processes. The learner works his or her way through the analytics problem from A-Z by receiving guidance in the instructions. This interactive case study will bring together the learners' Databricks knowledge picked up during the early stages of their journey in learning the tool and help learners apply the skills needed to pass the Databricks Certified Data Analyst Associate exam.Business IntelligenceCourseDatabricksApplication Form
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Data Transformation in KNIME
This course delves into the powerful world of data transformation using KNIME. Learners will start with the basics, gaining a solid understanding of KNIME’s essential data transformation techniques. The course will dive into practical applications, teaching how to effectively manipulate, clean, and integrate data within KNIME. They will also discover how to clean data by removing duplicates, handling missing values, applying filters, and integrating data from various sources. Additionally, learners will explore the Math Formula node to perform complex calculations and create new variables based on mathematical expressions. By the end, learners will be proficient in importing data, utilizing various transformation nodes, and building workflows to optimize data processes.Business IntelligenceCourseKNIMEApplication Form
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Data Manipulation in KNIMEHelp learners unlock the power of data manipulation with KNIME! This course will teach the essential techniques for merging, aggregating, and exporting data using KNIME’s robust tools. Learners will discover how to concatenate multiple tables, perform value lookups, and execute complex join operations. It will allow them to master group by and pivot operations with the Row Aggregator and understand the GroupBy, Pivot, and unpivoting nodes. It will also teach learners to write data in CSV, Excel, and other formats, handle remote file systems, and explore utility nodes for file management and database writing. By the end of this course, learners will be proficient in using KNIME to streamline data processes, enhance analysis capabilities, and efficiently handle data manipulation tasksBusiness IntelligenceCourseKNIMEApplication Form
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Transform and Analyze Data with
Microsoft Fabric
This course focuses on implementing data cleaning and preparation processes in Microsoft Fabric. Learners will explore star schemas, normalization, data transformations, and performance optimization. This course will be part of our track covering the Fabric Analytics Engineer Associate certificate from Microsoft (DP-600) and will include interactive exercises in the Fabric environment. For this course, we will assume learners are comfortable ingesting data into Fabric; this course should be about using the data now that it has been ingested.
CloudCourseMicrosoft FabricApplication Form
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Plan and Implement a Data Analytics Environment with Microsoft FabricThis course focuses on the administrative side of Microsoft Fabric. Learners will configure a Fabric environment for a larger team using the admin portal, implement access control for Fabric items, manage workspaces, warehouses, lakehouses, and other administrative Fabric jobs. This course will be part of our track covering the Fabric Analytics Engineer Associate certificate from Microsoft (DP-600) and will include interactive exercises in the Fabric environment. This course will come near the end of our Fabric track; as a result, learners will have a basic understanding of Fabric and its use cases.
CloudCourseMicrosoft FabricApplication Form
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Project: Find trends in dataIn this project, you will create visualizations and then interpret them to uncover possible trends and other insights from the data.
Prerequisites: Intermediate Data Visualization with ggplot2
Libraries/packages: ggplot2		Data ScienceProjectRApplication Form
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NOTE: Courses are color-coded by "Technology"
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Conceptual
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Excel
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Databricks
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Power BI/Tableau/Alteryx/KNIME
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Python
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R
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Snowflake
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SQL
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Cloud
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Java
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PySpark
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Kubernetes
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Hugging Face
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Microsoft Fabric
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Git
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