Professor Christiaan Hogendorn
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Course Policies and Grading Information
|Jan. 22 Th||1. Introduction
● David Auerbach, "You Are What You Click: On
Microtargeting: Why privacy and anonymity are being
violated online by an unstoppable process of data
profiling," The Nation, February 13, 2013.
|Jan. 27 T||BLIZZARD|
|Jan. 29 Th||2. Applications of Big Data
● Mayer-Schönberger, V., & Cukier, K. (2013). Big data:
A revolution that will transform how we live, work, and think.
Houghton Mifflin Harcourt, Chapters 1 and 6.
Right-click this link to save pdf file
● Data Size Matters Berkeley Data Science infographic, 2013.
|Feb. 3 T||3. Data Science
● Robin Bloor, "A Data Science Rant," August 12, 2013.
● Chris Anderson, "The End of Theory: The Data Deluge
Makes the Scientific Method Obsolete," WIRED
MAGAZINE: 16.07, June 23, 2008.
Assignment 1 due.
|Feb. 5 Th||4. Software for Big Data
● Will Stanton, "Becoming a data 'hacker,'" Will Stanton's
Data Science Blog, June 29, 2014.
● Jeffrey Stanton, "Getting Started with R," Chapter 3 in
An Introduction to Data Science, open source ebook.
(Hereafter called "Stanton.")
|Feb. 10 T||5. Introduction to R and Data Frames
● Stanton, Chapter 5 "Rows and Columns."
● Stanton, beginning of Chapter 9 "Onward with R-Studio."
Assignment 2 due. Data for assignment.
|Feb. 12 Th||6. Conditional Means
● Stanton, Chapter 6, "Beer, Farms, and Peas."
○ Joshua Anderson and Jorn-Steffen Pischke, appendix to
Chapter 1 in Mastering 'Metrics, Princeton University Press, 2015.
|Feb. 17 T||7. Facebook
● Stanton, Chapter 8, "Big Data? Big Deal!"
Assignment 3 due. Data for assignment.
|Feb. 19 Th||8. Regression
● Stanton, Chapter 16, "Line Up, Please"
|Feb. 24 T||9. Data Mining
Fun spurious correlations
Assignment 4 due. Optional Data for assignment.
|Feb. 26 Th||10. Machine Learning
● Stanton, Chapter 17, "Hi Ho, Hi Ho - Data Mining We Go"
|March 3 T||11. Google Flu Trends
● Lazer, D. M., Kennedy, R., King, G., and Vespignani, A.
(2014), The parable of Google Flu: Traps in big data
analysis. Science, 343(14 March).
● Bohannon, J. (2015). Credit card study blows holes
in anonymity. Science, 347(6221), 468–468.
|March 5 Th||12. Big Data Business Models
● Product differentiation
● Need for intermediaries. INRIX case.
|March 24 T||13. Big Data and Economic Growth
● James Glanz, Is Big Data an Economic Big Dud?
New York Times, August 17, 2013.
○ Carvalho, Vasco M. 2014. "From Micro to Macro via Production
Networks." Journal of Economic Perspectives, 28(4): 23-48.
|March 26 Th||14. Price Discrimination
● Benjamin Shiller (2014). First Degree Price Discrimination
Using Big Data, working paper no. 58, Brandeis University,
Department of Economics and International Businesss School.
● Adam OzimekWill Big Data Bring More Price Discrimination,
Forbes, September 2013.
|March 31 T||15. The FTC and Big Data
● Akiva Miller (2014). What Do We Worry About When We Worry
About Price Discrimination? The Law and Ethics of Using Personal
Information for Pricing. Journal of Technology Law & Policy, 19, 41.
● US Federal Trade Commission. (2014). Data brokers: A call for
transparency and accountability.
Midterm Assignment due.
|April 2 Th||16. Credit Cards and Big Data|
|April 7 T||17. Marshall's Tides
● John Sutton (2002) "The Standard Paradigm, Chapter 1 in
Marshall's Tendencies: what can economists know? MIT Press.
|April 14 T||18. The Experimental Ideal
● Joshua Anderson and Jorn-Steffen Pischke, Chapter 1
in Mastering 'Metrics, Princeton University Press, 2015.
|April 16 Th||20. Causality|
|April 21 T||21. Media Slant
● Gentzkow, M. and Shapiro, J.M., 2010. What drives media
slant? Evidence from US daily newspapers. Econometrica,
|April 23 Th||22. Long Tail of Google News
● Hengyi Zhu '15 An Examination of the Long-Tail Hypothesis in
the Online News Market: The Case of Google News, Thesis,
|April 28 T||23. Nowcasting
● Steve Scott and Hal Varian "Bayesian Variable Selection for
Nowcasting Economic Time Series ," Working paper 2014.
Assignment 5 due. Data for assignment: group 1 group 2 group 3 group 4
|April 30 Th||24. Machine Learning paper
● Peysakhovich, A., & Naecker, J. (2015). Machine learning
and behavioral economics: Evaluating models of choice under
risk and ambiguity. Available at SSRN 2548564.
|May 5 T||25. Conclusion|
|May 15||Final Project and Midterm Revision due.|