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Introduction to Data Science

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S.V.V.D.Jagadeesh

Sr. Assistant Professor

Dept of Artificial Intelligence & Data Science

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LAKIREDDY BALI REDDY COLLEGE OF ENGINEERING

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S.V.V.D.Jagadeesh

Wednesday, December 18, 2024

Previous Class Discussions

  • Session Outcomes
  • Facets of Data
  • Structured Data-Example
  • Unstructured Data-Example
  • Natural Language
  • Machine-Generated Data-Example
  • Graph-Based or Network Data-Example
  • Audio, Image and Video Data
  • Streaming Data

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At the end of this session, Student will be able to:

  • CO1: Understand the data science process. (Understand- L2)

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Wednesday, December 18, 2024

Session Outcomes

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  • The data science process typically consists of six steps
  • Setting the Research Goal
  • Retrieving Data
  • Data Preparation
  • Data Exploration
  • Data Modeling
  • Presentation and Automation�

S.V.V.D.Jagadeesh

Wednesday, December 18, 2024

Data Science Process

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Data Science Process

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Setting the Research Goal

  • Data science is mostly applied in the context of an organization.
  • When the business asks you to perform a data science project, you’ll first prepare a project charter.
  • This charter contains information such as what you’re going to research, how the company benefits from that, what data and resources you need, a timetable, and deliverables.

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  • The second step is to collect data.
  • You’ve stated in the project charter which data you need and where you can find it.
  • In this step you ensure that you can use the data in your program, which means checking the existence of, quality, and access to the data.
  • Data can also be delivered by third-party companies and takes many forms ranging from Excel spreadsheets to different types of databases

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Wednesday, December 18, 2024

Retrieving Data

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  • Data collection is an error-prone process; in this phase you enhance the quality of the data and prepare it for use in subsequent steps.
  • This phase consists of three sub-phases:
  • data cleansing removes false values from a data source and inconsistencies across data sources
  • data integration enriches data sources by combining information from multiple data sources
  • data transformation ensures that the data is in a suitable format for use in your models.

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Wednesday, December 18, 2024

Data Preparation

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  • Data exploration is concerned with building a deeper understanding of your data.
  • You try to understand how variables interact with each other, the distribution of the data, and whether there are outliers.
  • To achieve this you mainly use descriptive statistics, visual techniques, and simple modeling.
  • This step often goes by the abbreviation EDA, for Exploratory Data Analysis

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Data Exploration

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  • In this phase you use models, domain knowledge, and insights about the data you found in the previous steps to answer the research question.
  • You select a technique from the fields of statistics, machine learning, operations research, and so on.
  • Building a model is an iterative process that involves selecting the variables for the model, executing the model, and model diagnostics.

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Data Modeling or Model Building

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  • Finally, you present the results to your business.
  • These results can take many forms, ranging from presentations to research reports.
  • Sometimes you’ll need to automate the execution of the process because the business will want to use the insights you gained in another project or enable an operational process to use the outcome from your model.

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Presentation and Automation

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  • The previous description of the data science process gives you the impression that you walk through this process in a linear way, but in reality you often have to step back and rework certain findings.
  • For instance, you might find outliers in the data exploration phase that point to data import errors.
  • As part of the data science process you gain incremental insights, which may lead to new questions.
  • To prevent rework, make sure that you scope the business question clearly and thoroughly at the start.

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An Iterative Process

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  • Session Outcomes
  • The Data Science Process
  • Setting the Research Goal
  • Retrieving Data
  • Data Preparation
  • Data Exploration
  • Data Modeling or Model Building
  • Presentation and Automation
  • An Iterative Process

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Summary

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