STAT 215A ·

This calendar will be updated as the semester progresses.

Week	Date	Content	Assignments	Readings Due (before lecture)
1	Thu 08/26	Lecture 1: Introductions
1	Fri 08/27	Section 0: Git / GitHub + R / Tidyverse	Lab 0: GitHub setup + practice submission Due: 09/04
2	Tue 08/31	Lecture 2: Problem Formulation		"The Future of Data Analysis" (Tukey, 1962) [please skim] "Science and Statistics" (Box, 1976) "Veridical data science" (Yu and Kumbier, 2020)
	Thu 09/02	Lecture 3: Exploratory Data Analysis (EDA)
	Fri 09/03	Section 1: Workflow + Rmd + Latex + R Tricks + Lab 1	Lab 0 due Lab 1: Redwood data assigned Due: 09/16 at 11:59pm
3	Tue 09/07	Lecture 4: EDA (Part 2)
	Thu 09/09	Lecture 5: EDA (Part 3)
	Fri 09/10	Section 2: Advanced visualization techniques
4	Tue 09/14	Lecture 6: Prediction and assessment		"Growth in a Time of Debt" (Reinhart & Rogoff, 2010) "Does high public debt consistently stifle economic growth?" (Herndon, Ash, & Pollin, 2014) "Superheat" (Barter & Yu, 2018) Seeing is Believing: 5 Studies about Visual Information Processing
	Thu 09/16	Lecture 7: Prediction and assessment (Part 2)	Lab 1 due at 11:59pm	"What's Wrong with Social Science and How to Fix It: Reflections After Reading 2578 Papers"
	Fri 09/17	Section 3: TBA
	Sun 09/19		Lab 1 Peer Review assigned Due: 09/26 at 11:59pm
5	Tue 09/21	Lecture 8: Stability		Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks (Wu et al. 2016) "Funes the Memorious" (Borges, 1942) "Algebraic connectivity of graphs" (Fiedler 1973) "Normalized Cuts and Image Segmentation" (Shi and Malik, 2000)
	Thu 09/23	Lecture 9: Stability (Part 2)
	Fri 09/24	Section 4: TBA	Lab 2 assigned Due: 10/08 at 11:59pm
	Sun 09/26		Lab 1 Peer Review due at 11:59pm
6	Tue 09/28	Lecture 10: Stability (Part 3)		"Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist" (Norgeot et al., 2020) "Regression Shrinkage and Selection via the Lasso" (Tibshirani, 1996) "Ridge Regression: Biased Estimation for Nonorthogonal Problems" (Hoerl & Kennard, 1970) John Platt (1964) “Strong inference”, Science Rohe, Qin and Yu (2016) on directed spectral clustering, PNAS Breiman (1995) paper on non-negative garrote, which motivated Lasso Elastic net paper by Zuo and Hastie (2005)
	Thu 09/30	Section 5: TBA
	Fri 10/01	Lecture 11: Stability (Part 4)		Garrigues and El Ghaoui (2018) on on-line Lasso Nishimoto et al (2011) on movie reconstruction
7	Tue 10/05	Lecture 12: Sources of randomness		Notes on Ridge Regression by van Wieringen UCERF3: A New Earthquake Forecast for California’s Complex Fault System Contributions to the mathematical theory of evolution (Pearon 1983) "Remarks on nonparametric estimates of a density function" Rosenblatt (1956)
	Thu 10/07	Lecture 13: Sources of randomness (Part 2)	Lab 2 due at 11:59pm	Saltelli et al (2020) on five ways to ensure models serve society
	Fri 10/08	Section 6: Introduce Lab 3
	Sun 10/10		Lab 2 Peer Review assigned Due: 10/17 at 11:59pm
8	Tue 10/12	Section 7: TBA	Lab 3 assigned Due: 10/26 at 11:59pm	Neyman (1923) Rubin (1974) Lin (2013) Freedman book chapter 4: linear regression models
	Thu 10/14	Lecture 14: Sources of randomness (Part 3)
	Fri 10/15	Lecture 15: Bootstrap. Interpretation.		Freedman book chapter 5 Risk Analysis of the Space Shuttle: Pre-Challenger Ali et al (2019) paper on facebook ad process
	Sun 10/17		Lab 2 Peer Review due at 11:59pm
9	Tue 10/19	Section 8: Midterm review
	Thu 10/21	Midterm Exam
	Fri 10/22 (1-2:30pm)	Lecture 17: Classification
10	Tue 10/26	Lecture 16: Bootstrap. Interpretation. (Part 2)	Lab 3 due at 11:59pm
10	Fri 10/29	Section 9: Introduce Lab 4
11	Mon 11/01		Lab 4: Group Project assigned Due: 11/19 at 11:59pm
	Tue 11/02	Lecture 18: Inference for logistic regression		A Gentle Introduction to Risk-limiting Audits (Lindeman and Stark, 2012) Do-no-harm playbook by Ripley and Kleinfeld (2020) SPRT Derivation (on bCourses)
	Thu 11/04	Lecture 19: Logistic regression, Exponential family		Dobson Ch. 3-4
	Fri 11/05	Section 9: TBA
12	Tue 11/09	Lecture 20: Logistic regression, Exponential family (Part 2)
	Thu 11/11	Lecture 21: GLMs, Iteratively Reweighted Least Squares		D. F. Freedman and D. Lane (1983) "A Nonstochastic Interpretation of Reported Significance Levels" D. F. Freedman (1995) Some issues in the foundation of statistics
	Fri 11/12	Section 10: TBA	Final Project assigned Due: 12/10 at 11:59pm Lab 4 due at 11:59pm
13	Tue 11/16	Lecture 22: GLMs, Iteratively Reweighted Least Squares (Part 2)
	Thu 11/18	Lecture 23: Statistical Inference. PCS inference.		Reread: "Veridical data science" (Yu and Kumbier, 2020) Dwivedi et al. (2020) Stable discovery of interpretable subgroups via calibration in causal studies
	Fri 11/19	Section 11: Intro to COVID-19 data (guest speaker Tiffany Tang)
14	Tue 11/23	Lecture 24: Statistical Inference. PCS inference. (Part 2)
	Thu 11/25	Thanksgiving Break
	Fri 11/26	Thanksgiving Break
15	Tue 11/30	Lecture 25: Advanced topics
	Thu 12/02	Lecture 26: Advanced topics (Part 2)
	Fri 12/03	Extra OH
RRR	Fri 12/10		Final Project due at 11:59pm