Module 1: Programming Fundamentals
Introduction to programming concepts
Variables, data types, and operators
Control structures (loops, conditionals)
Functions and modular programming

Module 2: Python Programming Basics
Python syntax and environment setup
Data structures (lists, tuples, dictionaries, sets)
File handling and exception handling
Working with modules and packages

Module 3: Python for Data Analysis
NumPy fundamentals
Pandas for data manipulation
Data cleaning and preprocessing
Working with CSV, Excel, and databases

Module 4: Python Data Visualization
Matplotlib and Seaborn
Exploratory Data Analysis (EDA)
Creating charts, plots, and dashboards
Data storytelling basics

Module 5: Advanced Python Concepts
Object-Oriented Programming (OOP)
Lambda functions
Regular expressions
Introduction to APIs and automation

Module 6: Introduction to R Programming
R environment and RStudio
Data types and structures in R
Control statements and functions
Working with packages

Module 7: R for Data Analysis
Data manipulation using dplyr
Data reshaping with tidyr
Working with data frames
Importing and exporting data

Module 8: R Data Visualization
ggplot2 fundamentals
Advanced plotting techniques
Data visualization best practices

Module 9: Statistical Analysis Using R
Descriptive statistics
Hypothesis testing
Correlation and regression analysis
Statistical modeling basics

Module 10: Python vs R – Comparative Use Cases
When to use Python vs R
Integrating Python and R
Real-world business and analytics scenarios

Module 11: Tools & Best Practices
Jupyter Notebook & RStudio
Version control with Git
Coding standards and documentation
Reproducible analysis

Module 12: Capstone Project
End-to-end data analysis project
Implementation using Python and/or R
Insight presentation and reporting