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Data Science Beginner

Pandas & NumPy

The two libraries every Python data workflow rests on. NumPy arrays, broadcasting and vectorisation first, then pandas Series and DataFrames for loading, filtering, grouping, merging and reshaping real datasets.

30 chapters 2h 30m
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What you'll learn

  • Introduction to Data Science, Pandas, and NumPy
  • Installing Python, NumPy, and Pandas
  • NumPy Arrays Basics
  • NumPy Array Operations
  • NumPy Indexing and Slicing
  • NumPy Mathematical Functions
  • NumPy Broadcasting and Vectorization
  • NumPy Random Module

Course content

30 chapters · 2h 30m
  1. 1 Introduction to Data Science, Pandas, and NumPy 5 min
  2. 2 Installing Python, NumPy, and Pandas 5 min
  3. 3 NumPy Arrays Basics 5 min
  4. 4 NumPy Array Operations 5 min
  5. 5 NumPy Indexing and Slicing 5 min
  6. 6 NumPy Mathematical Functions 5 min
  7. 7 NumPy Broadcasting and Vectorization 5 min
  8. 8 NumPy Random Module 5 min
  9. 9 Introduction to Pandas 5 min
  10. 10 Pandas Series and DataFrames 5 min
  11. 11 Reading and Writing Data Files 5 min
  12. 12 Data Selection and Filtering 5 min
  13. 13 Data Cleaning in Pandas 5 min
  14. 14 Handling Missing Data 5 min
  15. 15 Data Transformation and Manipulation 5 min
  16. 16 GroupBy and Aggregation 5 min
  17. 17 Merging and Joining DataFrames 5 min
  18. 18 Working with Dates and Time Series 5 min
  19. 19 Data Visualization with Pandas 5 min
  20. 20 Advanced NumPy Concepts 5 min
  21. 21 Advanced Pandas Operations 5 min
  22. 22 Exploratory Data Analysis (EDA) 5 min
  23. 23 Statistical Analysis with Pandas and NumPy 5 min
  24. 24 Working with Large Datasets 5 min
  25. 25 Pandas with SQL Databases 5 min
  26. 26 Preparing Data for Machine Learning 5 min
  27. 27 Real-World Data Science Projects 5 min
  28. 28 Pandas and NumPy Interview Preparation 5 min
  29. 29 Performance Optimization in Pandas and NumPy 5 min
  30. 30 Final Projects and Real-World Applications 5 min