Welcome - Data 100: Principles and Techniques of Data Science

Course Notes

Contents

1. Introduction
2. Pandas
3. Data Cleaning and EDA
4. Regular Expressions
5. Visualization
6. Sampling
7. Modeling & SLR (Simple Linear Regression)
8. Constant Model, Loss, and Transformations
9. Ordinary Least Squares
10. sklearn and Gradient Descent
11. Feature Engineering
12. Cross Validation and Regularization
13. Random Variables
14. Estimators, Bias, and Variance
15. Parameter Inference and Bootstrapping
16. SQL
17. Logistic Regression