1 

Lecture 1: What is Data Science? 
Lab 1: Intro to Python (numpy, graphing libraries, program structure, Jupyter Notebook) 

R:HW0 
2 
Lecture 2: Data, Stats, and Visualization 
Lecture 3: Pandas and Scraping 
Lab 2: Python: sklearn, matplotlib 

R:HW1  D:HW0 
3 
Lecture 4: Introduction to Regression and kNN Regression 
Lecture 5: Linear Regression, Bootstrap and Confidence Intervals 
Lab 3: Scikitlearn for Simple Linear Regression 
Advanced Section 1: Linear Algebra and Hypothesis Testing 
R:HW2  D:HW1 
4 
Lecture 6: Multi and Poly Regression 
Lecture 7: Model selection and Cross Validation 
Lab 4: Multiple Linear Regression and Cross Validation 
Advanced Section 2: Regularization Methods and Their Justifications 
R:HW3  D:HW2 
5 
Lecture 8: Regularization and EDA 
Lecture 9: Visualization for Communication 
Lab 5: Matplotlib & Seaborn 
No Advanced Section 
No Assignment 
6 
Lecture 10: kNN Classification & Logistic Regression I 
Lecture 11: Logistic Regression II 
Lab 6: Logistic Regression 
Advanced Section 3:Generalized Linear Models 
R:HW4 (individual)  D:HW3 
7 
No Lecture (Holiday) 
Lecture 12: Dealing with Missing Data, Imputation 
Lab 7: KNN Classification & Imputation 
No Advanced Section 
No Assignment 
8 
Lecture 13: EthiCS 
Lecture 14: PCA 
Lab 8: PCA 
Advanced Sections 4: PCA 
R:HW5  D:HW4 
9 
Lecture 15: Classification Trees 
Lecture 16: Bagging, & Random Forest 
Lab 9: Trees and Random Forests 
No Advanced Section 
R:HW6  D:HW5 
10 
Lecture 17: Boosting Methods 
Lecture 18: Neural Networks 1 – Perceptron and MLP 
Lab 10: Boosting 
No Advanced Section 
No Assignment 
11 
Lecture 19: NN 2: Anatomy of NN, design choices 
Lecture 20: NN 3. Back Propagation 
Lab 11: Intro to NN 
Advanced Sections 5: Decision Trees & Ensemble Methods 
R:HW7 (individual)  D:HW6 
12 
Lecture 21: Neural Networks 4. Regularization methods 
Lecture 22: Visualization for Model Interpretation 
Lab 12: Regularization with NN 
Advanced Sections 6: Solvers 
No Assignment 
13 
Lecture 23: Experimental Design & Testing I 
No Lecture (Thanksgiving) 
No Lab 

R:HW8  D:HW7 [Due on Tuesday] 
14 
Lecture 24: Experimental Design & Testing II 

Lab 13: Web Dev for Final Projects 

D:HW8 
15 



Reading Period 

16 



Finals Week 
