📖 *30 days roadmap to learn Python for Data Analysis* 😄👇 Days 1-5: Introduction to Python 1. Day 1: Install Python and a code editor (e.g., Anaconda, Jupyter Notebook). 2. Day 2-5: Learn Python basics (variables, data types, and basic operations). Days 6-10: Control Flow and Functions 6. Day 6-8: Study control flow (if statements, loops). 9. Day 9-10: Learn about functions and modules in Python. Days 11-15: Data Structures 11. Day 11-12: Explore lists, tuples, and dictionaries. 13. Day 13-15: Study sets and string manipulation. Days 16-20: Libraries for Data Analysis 16. Day 16-17: Get familiar with NumPy for numerical operations. 18. Day 18-19: Dive into Pandas for data manipulation. 20. Day 20: Basic data visualization with Matplotlib. Days 21-25: Data Cleaning and Analysis 21. Day 21-22: Data cleaning and preprocessing using Pandas. 23. Day 23-25: Exploratory data analysis (EDA) techniques. Days 26-30: Advanced Topics 26. Day 26-27: Introduction to data visualization with Seaborn. 27. Day 28-29: Introduction to machine learning with Scikit-Learn. 30. Day 30: Create a small data analysis project. Use platforms like Kaggle to find datasets for projects & GeekforGeeks to practice coding problems. *ENJOY LEARNING* ❤️👍👍