Schedule

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

Legend:

• PA stands for Programming Assignment
• SR stands for Issue Report
• R stands for reading assignment

#Lecture	Date	Lecture	Keywords	Readings	Important Dates
1	03/29	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 SR assigned R1 assigned
2	03/31	Pitfalls in Inferential Statistics	Multiple hypothesis, Bonferroni correction, false discovery rate, statistical vs practical significance
3	04/05	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	04/07	Causality 1/2	causal models, experiments (RCT)
5	04/12	Experiments with Human Subjects	causal inference from observational data, quasi-experiments	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 PA2 assigned R3 assigned
6	04/14	Causality 2/2	human subjects, AB testing, experimental design
7	04/19	Introduction to Machine Learning 1/2	optimization vs generalization, training and test data, models, learning	Reading 4: Nithya Sambasivan et al. Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI. CHI 2021.	PA2 due R3 due PA3 assigned PA4 assigned R4 assigned
8	04/21	Machine Learning in the Wild 2/2	training data, feature engineering, information leakage, concept drift, algorithmic decision making
9	04/26	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	PA3 due R4 due
10	04/28	Discussion 1/2	packaging data products, reproducibility, repeatibility, visualization, communication
11	05/03	Visualization and Communication	packaging data products, reproducibility, repeatibility, visualization, communication		PA5 assigned
12	05/05	Introduction to Privacy	privacy definitions, law, technology
13	05/10	Anonymization	data anonymization and deanonymization, k-anonimity, attacks	Reading 6: Daniel Solove. ‘I’ve Got Nothing to Hide’ and Other Misunderstandings of Privacy. San Diego Law Review 44, 2007.	PA5 due R6 assigned
14	05/12	Statistical Data Privacy	differential privacy, sensitivity		PA6 assigned
15	05/17	Data Lifecycles	provenance, right to be forgotten, data portability	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”)	PA4 due R6 due R7 assigned
16	05/19	Data Markets	data ownership, value of data, data markets, markets for privacy, data brokers		PA6 due
17	05/24	Other topics	data unions, cooperatives, strikes		SR due R7 due
18	05/26	Discussion 2/2