Schedule

Please, check out this schedule frequently as it will likely change a bit throughout the quarter.

Legend:

• PA0 stands for Programming Assignment Primer
• PAX stands for Programming Assignment X, where X can take values {1, 2, 3, 4}, e.g., PA3 is the third programming assignment
• R stands for reading assignment
• P stands for quarter-long project

#Lecture	Date	Lecture	Keywords	Readings	Important Dates
1	03/19	Course Overview and Introduction to the Data Science Process	Data science lifecycle. Ethics, fairness, responsibility, and privacy issues.	Reading 1.1: John P. A. Ioannidis Why Most Published Research Findings Are False PLOS Medicine. 2005 Reading 1.2: Michael Jordan Artificial Intelligence: The Revolution Hasn’t Happened Yet. HDSR 2019.	PA0 assigned R1 assigned P assigned
2	03/21	Pitfalls in Inferential Statistics	Multiple hypotheses, Bonferroni correction, false discovery rate, statistical vs practical significance
3	03/26	Data Context and Quality	collection, preparation, cleaning, missing data	Reading 2.1: Mark D. Wilkinson et al. The FAIR Guiding Principles for scientific data management and stewardship. Nature Scientific Data. 2016 Reading 2.2: Stephen Stigler. Data Have a Limited Shelf Life. HDSR 2019.	R1 due PA0 due R2 assigned PA1 assigned
4	03/28	Causality and Experiments 1/2	causal models, experiments (RCT)
5	04/02	Causality and Experiments 2/2	causal inference from observational data, human subjects, AB testing, experimental design	Reading 3.1: Department of Health, Education, and Welfare. The Belmont Report. April 18, 1979. Reading 3.2 Robert Bond, Christopher Fariss et al.A 61-million-person experiment in social influence and political mobilization. Nature 2012.	PA1 due R2 due R3 assigned PA2 assigned
6	04/04	IRB (Cheryl Danton)
7	04/09	Discussion 1/3	optimization vs generalization, training and test data, models, learning	Reading 4.1: Nithya Sambasivan et al. Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI. CHI 2021. 4.2 Wendy Parker Model Evaluation: An Adequacy-for-Purpose View. 2022 (read the introduction and (optionally) the rest)	R3 due R4 assigned
8	04/11	Machine Learning in the Wild	training data, feature engineering, information leakage, concept drift, algorithmic decision making		PA2 due
9	04/16	Fairness and Interpretability in Machine Learning	fairness definitions	Reading 5.1 Deirdre K. Mulligan, Joshua A. Kroll, Nitin Kohli, Richmond Y. Wong This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology CSCW 2019 Reading 5.2 Julia Angwin, Jeff Larson, Surya Mattu, Lauren Kirchner. Machine Bias. ProPublica, May 23, 2016	R4 due R5 assigned PA3 assigned
10	04/18	Visualization and Communication	packaging data products, reproducibility, repeatability, visualization, communication
11	04/23	Discussion 2/3			R5 due
12	04/25	Introduction to Privacy 1/2	privacy definitions, law, technology		PA3 due
13	04/30	Introduction to Privacy 2/2	data anonymization and deanonymization, k-anonimity, attacks, indigenous data sovereignty	Reading 6: Shoshana Zuboff. Big other: surveillance capitalism and the prospects of an information civilization. Journal of Information Technology 2015.	R6 assigned
14	05/02	Statistical Data Privacy	differential privacy, sensitivity		PA4 assigned
15	05/07	Data Flows, Lifecyles, Data Markets	provenance, right to be forgotten, data portability, data brokers, data ownership, value of data, data unions, cooperatives, strikes	Reading 7 . Edith Ramirez, Julie Brill, Maureen K. Ohlhausen, Joshua D. Wright, Terrell McSweeny Data Brokers: A call for transparency and accountability. Federal Trade Commission, May, 2014 (Read Executive Summary and then Section 4 “Types of Products”)	R6 due R7 assigned
16	05/09	Discussion 3/3
17	05/14	Summary of the quarter via a case study of LLMs Statistics and the Census Bureau			R7 due PA4 due
18	05/16	AMA
	05/17	No class			P due