Book Content
chapters • 20h4m total length
1. Review of the Core Modules of NumPy and Pandas
2. Review of Another Core Module - Matplotlib
3. Data – What Is It Really?
4. Databases
5. Data Visualization
6. Prediction
7. Classification
8. Clustering Analysis
9. Data Cleaning Level I - Cleaning Up the Table
10. Data Cleaning Level II - Unpacking, Restructuring, and Reformulating the Table
11. Data Cleaning Level III- Missing Values, Outliers, and Errors
12. Data Fusion and Data Integration
13. Data Reduction
14. Data Transformation and Massaging
15. Case Study 1 - Mental Health in Tech
16. Case Study 2 - Predicting COVID-19 Hospitalizations
17. Case Study 3: United States Counties Clustering Analysis
18. Summary, Practice Case Studies, and Conclusions
