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Data Science and Business Analytics with Python
Course Lectures
1. Introduction (3:23)
2. Class Project (1:35)
3. What is Data Science (4:44)
4. Tool Overview (4:15)
5. How To Find Help (14:17)
6. Data Loading (0:21)
7. Loading Excel and CSV files (6:20)
8. Loading Data from SQL (5:11)
9. Loading Any Data File (5:59)
10. Dealing with Huge Data (10:15)
11. Combining Multiple Data Sources (4:15)
12. Data Cleaning (0:54)
13. Dealing with Missing Data (8:06)
14. Scaling and Binning Numerical Data (12:27)
15. Validating Data with Schemas (10:10)
16. Encoding Categorical Data (6:39)
17. Exploratory Data Analysis (6:39)
18. Visual Data Exploration (10:20)
19. Descriptive Statistics (10:20)
20. Dividing Data into Subsets (12:31)
21. Finding and Understanding Relations in the Data (5:51)
22. Machine Learning (1:08)
23. Linear Regression for Price Prediction (14:30)
24. Decision Trees and Random Forests (6:59)
25. Machine Learning Classification (9:45)
26. Data Clustering for Deeper Insights (8:16)
27. Validation of Machine Learning Models (10:02)
28. ML Interpretability (16:23)
29. Intro to Machine Learning Fairness (7:47)
30. Visuals & Reports (16:23)
31. Visualization Basics (16:23)
32. Visualizing Geospatial Information (5:30)
33. Exporting Data and Visualizations (6:42)
34. Creating Presentations directly in Jupyter (2:38)
35. Generating PDF Reports from Jupyter (3:48)
36. Conclusion and Congratulations! (2:04)
Exercises
Notebooks
29. Intro to Machine Learning Fairness
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