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Data Science Beginner

Jupyter Notebooks

Get productive in the notebook environment most data work happens in: cells and Markdown documentation, pandas and NumPy analysis, inline charts, interactive widgets, extensions, and sharing or exporting finished notebooks.

20 chapters 1h 40m
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What you'll learn

  • Introduction to Jupyter Notebooks
  • Installing Python, Jupyter, and VS Code
  • Understanding the Jupyter Interface
  • Running Python Code in Jupyter
  • Markdown Cells and Documentation
  • Variables, Data Types, and Input Handling
  • Working with Functions and Modules
  • Data Structures in Jupyter

Course content

20 chapters · 1h 40m
  1. 1 Introduction to Jupyter Notebooks 5 min
  2. 2 Installing Python, Jupyter, and VS Code 5 min
  3. 3 Understanding the Jupyter Interface 5 min
  4. 4 Running Python Code in Jupyter 5 min
  5. 5 Markdown Cells and Documentation 5 min
  6. 6 Variables, Data Types, and Input Handling 5 min
  7. 7 Working with Functions and Modules 5 min
  8. 8 Data Structures in Jupyter 5 min
  9. 9 File Handling and Notebook Management 5 min
  10. 10 Data Analysis with Pandas in Jupyter 5 min
  11. 11 NumPy Integration in Jupyter 5 min
  12. 12 Data Visualization in Jupyter 5 min
  13. 13 Interactive Widgets in Jupyter 5 min
  14. 14 Machine Learning Workflows in Jupyter 5 min
  15. 15 Notebook Extensions and Productivity Tools 5 min
  16. 16 Sharing and Exporting Notebooks 5 min
  17. 17 JupyterLab and Advanced Features 5 min
  18. 18 Performance Optimization in Jupyter 5 min
  19. 19 Jupyter Notebook Interview Preparation 5 min
  20. 20 Final Projects and Real-World Applications 5 min