9/1 

Lecture 1: What is Data Science? General introduction. 
Lecture 2: Data + RegEx 

R:HW0 
9/7 
No Lecture (Holiday) 
Lecture 3: Web Scraping + PANDAS 
Lecture 4: Advanced PANDAS 

R:HW1  D:HW0 
9/14 
Lecture 5: kNN Regression and Linear Regression 
Lecture 6: Multi and Poly Regression 
Lecture 7: Modeling knn and Linear R with skleanr 

R:HW2  D:HW1 
9/21 
Lecture 8: Basic Statistics for Data Science 
Lecture 9: Inference: Bootstrap and CI 
Lecture 10: Hypothesis Testing & Predictive CI 
Advanced Section 1: Linear Algebra and Hypothesis Testing 
R:HW3  D:HW2 
9/28 
Lecture 11: CrossValidation & Model Selection 
Lecture 12: Regularization: Ridge & Lasso 
Lecture 13: Estimation of regularization parameter; Hands on 
Advanced Section 2: Methods of regularization and their justifications 
Milestone 1 
10/5 
Lecture 14: Visualization for Communication 
Lecture 15: kNN classification and Logistic Regression I 
Lecture 16: Case Study 1 

R: HW4 (Individual)  D: HW3 
10/12 
No Lecture (Holiday) 
Lecture 17: Logistic Regression II 
Lecture 18: Multi class Classification (introduce softmax) 
Advanced Section 3: Generalized Linear Models 
Milestone 2 
10/19 
Lecture 19: Dealing with missing data, imputation 
Lecture 20: PCA 
Lecture 21: PCA and missing with data; hands on 
Advanced Section 4: Mathematical Foundations of PCA 
R:HW5  D:HW4 
10/26 
Lecture 22: Classification Trees 
Lecture 23: Regression Trees Bagging RF 
Lecture 24: Tuning hyperparameters 

R:HW6  D:HW5 
11/2 
Lecture 25: Boosting Methods for Regression 
Lecture 26: Boosting Methods for Classification 
Lecture 27: Case Study 2 
Advanced Section 5: Stacking and mixture of experts 

11/9 
Lecture 28: Neural Networks 1Perceptron and MLP 
Lecture 29: Neural Networks 2 Anatomy of NN, design choices 
Lecture 30: Neural Netoworks 3 Design Choices II & Gradient Descent 

R:HW7 (Individual)  D:HW6 
11/16 
Lecture 31: Neural Networks 4 Back Propagation, SGD 
Lecture 32: Regularization methods  Weight decay, data augmentation and dropout 
Lecture 33: Full worked example of regression and classification FFNN 
Advanced Section 6: Deeper into Solvers 
Milestone 3 
11/23 
Lecture 34: EthiCS 
No lecture (Thanksgiving) 
No lecture (Thanksgiving) 

R:HW8  D:HW7 
11/30 
Lecture 35: Interpreting Prediction Models 
Lecture 36: WrapUp 


D: HW8 
12/7 



Reading Period 

12/14 



Finals Week 
