This page lists the class lectures and recitations, plus additional material (slides, notes, video) associated with each lecture.

Lectures

Date Topic Slides Notes Video Quiz
  Data collection and management        
1/17 Introduction
1/22 Data collection and scraping
1/24 Jupyer notebook lab
1/29 Relational data
1/31 Visualization and data exploration
2/5 Vectors, matrices, and linear algebra
2/7 Graph and network processing
2/12 Free text and natural language processing
2/14 Free text, continued
  Statistical modeling and machine learning        
2/19 Introduction to machine learing
2/21 Linear regression continued
2/26 Linear classification
2/28 Nonlinear modeling, cross-validation
3/5 Nonlinear modeling (cont), evaluating ML models
3/7 Class postponed        
3/12 Spring break        
3/14 Spring break        
3/19 Basics of probability
3/21 Maxiumum likelihood estimation, naive Bayes
3/26 Hypothesis testing and experimental design  
  Advanced modeling techniques        
3/28 Clustering and dimensionality reduction
4/2 Recommender systems
4/4 Anomaly detection  
4/9 Decision trees, interpretable models  
4/11 Deep learning  
  Additional topics        
4/16 Big data and MapReduce methods  
4/18 Probabilistic modeling  
4/23 Debugging data science
4/25 A data science walkthrough  
4/30 Data science walkthrough (pt 2)    
5/2 The future of data science and Q&A  

Recitations and optional lectures

Date Homework Notebook Video
1/26 HW 1
2/9 HW 2
2/16 HW 2 Office Hours  
2/23 HW 3
3/2 HW 3 Office Hours  
3/30 HW 4
4/5 Time series (optional lecture)
4/6 HW 4 Office Hours  
4/23 HW 5 Recitation