Professor Christiaan Hogendorn
Spring 2016
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Course Policies and Grading Information
| Jan. 21 Th | 1. Introduction Goals: understand course structure; note the difference between economists using big data versus economists studying big data; define econometrics. ● Evan Selinger and Brett Frischmann, “Will the internet of things result in predictable people?” The Guardian, August 10, 2015. |
| Jan. 26 T | 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. ○ Moneyball Scene |
| Jan. 28 Th | 3. Data Science Goal: understand the emerging field of data science and the role of theory in empirical 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. ● Kaplan Chapter 1 “Tidy Data” ○ David Donaho, “50 Years of Data Science,” 2015. |
| Feb. 2 T | 4. Software for Big Data Goal: understand store, query, tidy, analyze; stacks; SQL, Hadoop, and especially R. ● Will Stanton, “Becoming a data ‘hacker,’” Will Stanton’s Data Science Blog, June 29, 2014. ● Kaplan Chapter 2 “Computing with R.” ● Comprehensive R Archive Network ● RStudio |
| Feb. 4 Th | 5. Introduction to R and Data Frames Goals: leanr about data file formats, importing data into R, and filtering your data. ● Kaplan Chapter 3 “R Command Patterns” ● Kaplan Chapter 4 “Files and Documents” ○ Puppies data csv file |
| Feb. 9 T | 6. Data Wrangling Goals: appreciate the difficulty of getting data in the proper format; learn some techniques. ● Kaplan Chapter 7 “Wrangling and Data Verbs” ● Kaplan Chapter 9 “More Data Verbs” ○ RStudio Data Wrangling Cheat Sheet. |
| Feb. 11 Th | 7. Joining Data Tables Goal: learn about different types of joins. ● Kaplan Chapter 10 “Joining Two Tables” ● Bohannon, J. (2015). Credit card study blows holes in anonymity. Science, 347(6221), 468–468. ○ Puppies data RData file ○ Class 7 Handout Assignment 1 due. Data for assignment. |
| Feb. 16 T | 8. Conditional Means Goals: understand mean, standard deviation, t-statistic, and t-test. ● Joshua Angrist and Jorn-Steffen Pischke, appendix to Chapter 1 in Mastering ’Metrics, Princeton University Press, 2015. Assignment 2 due. Data for assignment. |
| Feb. 18 Th | 9. Regression Goal: understand ordinary least squares regression. ● Angrist and Pischke, Chapter 2 in Mastering ’Metrics, (focus on pp. 56–59 and 68–70). |
| Feb. 23 T | 10. Marshall’s Tides Goal: understand the idea of a systematic part and a disturbance part of a model. ● John Sutton (2002) "The Standard Paradigm, Chapter 1 in Marshall’s Tendencies: what can economists know? MIT Press. Assignment 3 due. Optional Data for assignment. |
| Feb. 25 Th | 11. Causality Goal: learn to draw causal diagrams of models. ○ Morgan, Stephen L., and Christopher Winship. Counterfactuals and causal inference. Cambridge University Press, 2014. |
| March 1 T | 12. The Experimental Ideal Goals: understand selection bias and how to minimize it. ● Anderson and Pischke, Chapter 1 in Mastering ’Metrics, (focus on pp. 9–16). ● Review Chapter 2, focusing first on pp. 68–70 and second on pp. 71–74. |
| March 3 Th | 13. Data Mining and Cross Validation Goal: appreciate the importance of splitting into training and testing samples. Fun spurious correlations ● 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). Project Part 1 due. |
| SPRING BREAK | |
| March 22 T | 14. Visualizing Data and Inductive Reasoning ○ Kaplan Chapter 5 “Introduction to Data Graphics ○ Kaplan Chapter 6 ”Frames, Glyphs, and other Components of Graphics“ ● Kaplan Chapter 8 ”Graphics and Their Grammar" |
| March 24 Th | 15. Exploratory Data Analysis ● Kaplan Chapter 14, “Collective Properties of Cases.” |
| March 29 T | 16. Machine Learning ● Kaplan Chapter 17 “Machine Learning.” Assignment 4 due. |
| March 31 Th | 17. Penalized Regression ○ Belloni, A., Chernozhukov, V., & Hansen, C. (2014). High-dimensional methods and inference on structural and treatment effects. The Journal of Economic Perspectives, 28(2), 29–50. |
| April 5 T | 18. Big Data Business Models ● Product differentiation ● Need for intermediaries. INRIX case. Assignment 5 due. Data for assignment. |
| April 7 Th | 19. 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. ○ John Naughton, “What’s Twitter’s real value? Don’t ask an economist: Those who measure GDP need to find a way of assessing the contribution of social media,” The Guardian, Saturday 23 November 2013. |
| April 14 Th | 20. Big Data and Jobs ● Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. The Journal of Economic Perspectives, 29(3), 3–30. ○ Brynjolfsson, E., McAfee, A., & Spence, M. (2014). Labor, Capital, and Ideas in the Power Law Economy. Foreign Affairs, 93, 44. ○ Gavin Kelly, “The robots are coming. Will they bring wealth or a divided society?” The Guardian, January 4, 2014. ○ Erik Brynjolfsson and Andrew McAfee, The Second Machine Age, W.W. Norton, 2014, especially Chapters 9 and 11. |
| April 19 T | 21. 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. |
| April 21 Th | 22. Visit from Ivan Stoitzev |
| April 26 T | 23. 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. ○ US Federal Trade Commission. (2016). Big Data: A Tool for Inclusion or Exclusion? |
| April 28 Th | 24. Media Slant ● Budak, C., Goel, S., & Rao, J. M. (2014). Fair and balanced? Quantifying Media Bias Through Crowdsourced Content Analysis. ○ Gentzkow, M. and Shapiro, J.M., 2010. What drives media slant? Evidence from US daily newspapers. Econometrica, 78(1), pp. 35–71. |
| May 3 T | 26. Final Quiz in class |
| May 11 W | Project Part 2 due. |