Module 0 
1 

Lecture 0: What is Data Science? (PP,KR) 
Lab 0: Intro to Python 


Homework 0 


2 
Labor Day (No Class) 
Lecture 1: Data; Stats; Visualization 
Lab 1: Python: Numpy, functions, Pandas, Matplotlib 


Homework 1 


3 
Lecture 2: Pandas and Scraping 
Lecture 3: Numpy; Scraping; Proper Visualization; EDA 
Lab 2: EDA 
SSection 1: BeautifulSoup 



Module 1 
4 
Lecture 4: Intro to Linear Regression and kNN 
Lecture 5: Multiple Regression and Bootstrap 
Lab 3: Linear Regression 
SSection 2: Visualization 
ASection 1 
Homework 2 


5 
Lecture 6: CrossValidation and Model Selection 
Lecture 7: Linear Model Regularization: Ridge & Lasso 
Lab 4: Model Selection 
SSection 3 
ASection 2 



6 
Lecture 8: PCA and High Dimensionality; Dealing with Big Data 
Lecture 9: Visualization for Communication 
Lab 5: Regularization 
SSection 4 
ASection 3 
Homework 3 & Homework 4 

Module 2 
7 
Columbus Day (No Class) 
Lecture 10: Logistic Regression I 
Lab 6: Logistic Regression & PCA 
SSection 5 
ASection 4 
Homework 5 


8 
Lecture 11: Logistic Regression II 
Lecture 12: kNN classification and dealing with missing data 
Lab 7: Logistic Regression & kNN Classification 
SSection 6 
ASection 5 
Homework 6 & Homework 7 


9 
Lecture 13: LDA and QDA 
Lecture 14: Classification Trees 
Lab 8: Discriminant Analysis & Classification Trees 
SSection 7 
ASection 6 


Module 3 
10 
Guest Lecture : Classification Summary; Ethics and Critical Thinking 
Guest Lecture: Storytelling with Data  Finding the Narrative in the Numbers 




Midterm 

11 
Lecture 15: Regression Trees and Random Forests 
Lecture 16: Boosting 
Lab 9: Random Forests and Boosting 
SSection 8 
ASection 7 
Homework 8 


12 
Lecture 17: Stacking 
Lecture 18: SVM I 
Lab 10: Projects 
SSection 9 




13 
Lecture 19: SVM II 
Thanksgiving (No Class) 






14 
Lecture 20: AB Testing 





