Table of Contents

  1. Data Science Fundamentals
  2. Installing Software and System Setup
  3. Lists and Dictionaries
  4. Package, Function, and Loop
  5. NumPy Foundation
  6. Pandas and DataFrame
  7. Interacting with Databases
  8. Thinking Statistically in Data Science
  9. How to Import Data in Python?
  10. Cleaning of Imported Data
  11. Data Visualization
  12. Data Pre-processing
  13. Supervised Machine Learning
  14. Unsupervised Machine Learning
  15. Handling Time-Series Data
  16. Time-Series Methods
  17. Case Study-1
  18. Case Study-2
  19. Case Study-3
  20. Case Study-4
  21. Python Virtual Environment
  22. Introduction to An Advanced Algorithm - CatBoost
  23. Revision of All Chapters’ Learning