This page lists the class lectures plus additional material (slides, notes) associated with each lecture. Recordings of all the classes will available on the course Canvas page.

Lectures from a previous offering (Fall 2019) are available on Panopto.


Note: Lecture schedule, slides, and notes are subject to change.

Date Lecture Slides Notes
  Data collection and management    
1/19 Wed 1: Introduction pdf (inked)
1/24 Mon 2: Data collection and scraping pdf (inked)
1/26 Wed 3: Jupyter Notebook lab pdf (inked)
1/31 Mon 4: Relational data pdf (inked)
2/2 Wed 5: Visualization and data exploration pdf (inked)
2/7 Mon 6: Vectors, matrices, and linear algebra pdf (inked)
2/9 Wed 7: (continued)    
2/14 Mon 8: Graph and network processing pdf (inked)
2/16 Wed 9: Free text and natural language processing pdf (inked)
  Statistical modeling and machine learning    
2/21 Mon 10: Introduction to machine learning pdf (inked)
2/23 Wed (continued)    
2/28 Mon 12: Linear classification pdf (inked)
3/2 Wed 13: (continued)    
3/7 Mon No class: Spring Break    
3/9 Wed No class: Spring Break    
3/14 Mon 14: Nonlinear modeling, cross-validation pdf (inked)
3/16 Wed 15: (continued)    
3/21 Mon 16: Basics of probability pdf (inked)
3/23 Wed 17: (continued)    
3/28 Mon 18: Maximum likelihood estimation, naive Bayes pdf (inked)
3/30 Wed 19: Hypothesis testing and experimental design pdf (inked) none
  Advanced modeling techniques    
4/4 Mon 20: Unsupervised learning pdf (inked)
4/6 Wed 21: Recommender systems pdf (inked)
4/11 Mon 22: Decision trees, interpretable models pdf (inked) none
4/13 Wed 23: Deep learning Preview: pdf none
4/18 Mon 24: (continued)    
  Additional topics    
4/20 Wed 25: Big data and MapReduce methods Preview: pdf none
4/25 Mon 26: Debugging data science Preview: pdf
4/27 Wed 27: The future of data science and Q&A Preview: pdf none