Course Modules:


Course Modules Sub Modules
I. Introduction to Data Analytics - Evolution of Digital Technology and Industry 4.0.
- The journey from ERP to digital transformation.
- Application of digital tools for business decision-making and execution management.
- Importance of IT and digital tools for experience and satisfaction management of stake holders.
- Legal issues in information technology and digital transformation.
II. Statistical Theory and Application in Data Analytics - Measures of central tendency, Dispersion, Skewness, Kurtosis
- Moments, Theory of Probability, Discrete Probability Distribution - Binomial, Poisson, Hypergeometric
- Continuous Probability Distribution - beta, gamma, rectangular, normal Distribution; t-distribution; F-Test; Chi squared distribution; Jacobian of transformation
- Testing of Hypothesis, Design of experiments and analysis of variance
- Basics of sample survey, Numerical analysis
III. Data Analytics with Excel & Advanced Excel: Case Studies - Fundamentals of Excel
- Use of Data Analysis
- Macro & VBA Use Case
- Financial Modelling & Capital Budgeting
- Perform Statistical analysis of stock market data
- Time Series Forecasting
IV. Introduction to Power BI - Power BI Basics: What is BI, Purpose, Pull excel table to Power BI, what is Model and how to build relationship among different tables,Different type of charts and visuals
- Power BI Advanced: Introduction to Power Query Editor, how to do transformation - Split, Unpivot etc., Importing visuals, Slicer etc. and purpose of some of the visuals, Introduction to DAX
- Case Study: Creating a Dashboard
V. Introduction to Python - Introduction to Programming
- Introduction to Python: Introduction to Python Interpreter, Anacondainstallation, Usage of Python Notebook, Spyder etc
- Expression & Variables, first python program, Coding structure &indentations, Typecasting and basic operations
- Python String, data Structure, Flow Controls. Functions & Packages, Classes & Objects, File Handling, Math Operations
- Advanced Python: Understanding the domain, Importing Data, Basic Model Development, Data Visualization,
- Introduction to Machine Learning