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  
2/19 Overflow lecture        
  Statistical modeling and machine learning        
2/21 Linear regression        
2/26 Linear classification        
2/28 Nonlinear modeling, cross-validation        
3/5 Evaluating machine learning models        
3/7 Basics of probability        
3/12 Spring break        
3/14 Spring break        
3/19 Hypothesis testing and experimental design        
3/21 Decision trees, interpretable models        
  Advanced modeling techniques        
3/26 Clustering and dimensionality reduction        
3/28 Anomaly detection        
4/2 Recommender systems        
4/4 Deep learning        
4/9 Overflow lecture        
  Additional topics        
4/11 High dimensional visualization        
4/16 Probabilistic modeling        
4/18 Big data and MapReduce methods        
4/23 Debugging data science        
4/25 A data science walkthrough        
4/30 Data science jobs        
5/2 The future of data science and Q&A        

Recitations

Date Homework Notebook Video
1/26 HW 1
2/9 HW 2
2/16 HW 2 Office Hours