Course Schedule

	What we will discuss/do in class	What to prepare (before class)
Week 1	Introduction and setting up the Python environment
Week 2	Pandas Data Frames and NumPy arrays	From “Python for Data Analysis,” read Chapter 5, Section 4.1 and Appendix A.
Week 3	Describing Data and Feature Scaling/Plotting	From “Python for Data Analysis,” read Sections 9.1 and 9.2
Week 4	Introduction to modeling overview: targets/features/regression vs classification; testing data versus training data; idea of hyperparameters, Ordinary Least Squares
Week 5	Model Validation and Regularization	From “Python for Data Analysis,” read Sections 13.1
Week 6	Break
Week 7	Q/A and Review for the Midterm
Week 8	Patterns in High Dimensional Data, Principal Component Analysis, and T-stochastic Neighbor Embeddings
Week 9	Classification Problems, Logistic Regression, Decision Trees and Random Forests
Week 10	Clustering Algorithms
Week 11	Q/A and Review