B.Sc. (Data Science) Data Analysis and Visualization Syllabus - Mumbai University
This is the Fourth Year BSc Data Science Honours syllabus under NEP 2020, phased in one year at a time, from the academic year 2024-25. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.
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Syllabus for Data Analysis and Visualization
Module I: Introduction to Data Analysis
- Data Analysis - Exploratory Data Analysis and Data Science Process - Responsibilities of a Data Analyst - Data Analytics vs. Data Analysis - Types of Data - Understanding Different Types of File Formats - Sources of Data - Languages for Data Professionals - Overview of Data Repositories - Data Marts, Data Lakes, ETL, and Data Pipelines - Foundations of Big Data - Identifying Data for Analysis
Module II: Data Wrangling
- Data Sources - How to gather and Import Data - Data Loading, Storage and File Formats - Reading and Writing Data in Text Format, Web Scraping, Binary Data Formats, interacting with Web APIs, Interacting with Databases – Data Wrangling - Hierarchical Indexing, Combining and Merging Data Sets Reshaping and Pivoting - Tools for Data Wrangling - Data Cleaning and Preparation - Handling Missing Data, Data Transformation, String Manipulation
Module III: Data Visualization
- Intro to data visualization - Introduction to Visualization and Dashboarding Software - Visualization Tools - Getting started with Tableau Desktop – Connecting to the dataset - Creating charts – Creating common visualizations (bar charts, line charts etc.) - Filtering and sorting data - Adding Titles, Labels, and descriptions - Publish your work to Tableau Cloud - Interactivity with text and visual tooltips - Interactivity with actions (filter, highlight, URL) – Assembling dashboards from multiple charts
Module IV: Story Telling
- Introduction to Power BI - Understanding Desktop - Understanding Power BI Report Designer - Report Canvas, Report Pages: Creation, Renames - Report Visuals, Fields and UI Options - Experimenting Visual Interactions, Advantages - Reports with Multiple Pages and Advantages - Pages with Multiple Visualizations - PUBLISH Options and Report Verification in Cloud - Adding Report Titles. Report Format Options - Introduction to data storytelling - Creating a data story 10 Text Books 1. Python for Data Analysis: Data Wrangling with Pandas, NumPy and IPython by McKinney, W., 2nd edition. O’Reilly Media, 2017 2. Doing Data Science: Straight Talk from the Frontline by O’Neil, C., & Schutt, R, O’Reilly Media, 2013 3. The Big Book of Dashboards by Steve Wexler, Jeffrey Shaffer, Andy Cotgreave, John Wiley & Sons, 2017 11 Reference Books 1. Practical Tableau by Ryan Sleeper, O’Reilly Media, 2018 2. Power BI. Book-1, Business Intelligence Clinic: Create and Learn by Roger F Silva, 2018 3. Introducing Microsoft Power BI by Alberto Ferrari and Marco Russo, Microsoft Press, Washington, 2016 12 Internal Continuous Assessment: 50% Semester End Examination: 50%
Text Books
- 1 Python for Data Analysis: Data Wrangling with Pandas, NumPy and IPython by McKinney, W., 2nd edition. O’Reilly Media, 2017
- 2 Doing Data Science: Straight Talk from the Frontline by O’Neil, C., & Schutt, R, O’Reilly Media, 2013
- 3 The Big Book of Dashboards by Steve Wexler, Jeffrey Shaffer, Andy Cotgreave, John Wiley & Sons, 2017
- 1 Practical Tableau by Ryan Sleeper, O’Reilly Media, 2018
- 2 Power BI. Book-1, Business Intelligence Clinic: Create and Learn by Roger F Silva, 2018
- 3 Introducing Microsoft Power BI by Alberto Ferrari and Marco Russo, Microsoft Press, Washington, 2016
Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020. Wording is as printed in that syllabus. Module numbering is as printed there too.
The complete syllabus
This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.