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.

Lectures

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