ECO 4370 / 6370 Computing in Economics

Instructor: Zhan Gao
Course information: See Syllabus.

Schedule

Week	Tue. Session	Thu. Session	Make-up Session (Wed.)
Week 1 (Aug 25)	Course Overview	R Programming
Week 2 (Sep 1)	R Programming (cont.)	R Programming (cont.)
Week 3 (Sep 8)	Shell scripting	Git(hub)
Week 4 (Sep 15)	Data wrangling and tidying	In-Class Exam 1 Solution
HW #1 due on Monday Sep 15
Week 5 (Sep 22)	Taming the data zoo: cross-sectional, time series and panel Data: cps09mar.csv PredictorData2018.xlsx	Taming the data zoo: Text data
Week 6 (Sep 29)	Sampling, bootstrap Data: web-browser.csv	Linear regression Data: oj.csv redfinLA3.csv web-browsers.csv	Taming the data zoo: Spatial data and GIS Data: china_map_dt.Rdata counties_treated.Rdata
Week 7 (Oct 6)	Logistic regression Data: spam.csv tripadvisor.RData llm-arena	Concepts in Machine Learning
HW #2 due on Monday Oct 12
Week 8 (Oct 13)	Proposal Presentation	Regularized Regressions Data: wine.csv web-browsers.csv browser-totalspend.csv browser-domains.csv browser-sites.txt
Week 9 (Oct 20)	Fall Break	Trees Data: CAhousing.csv nbc_demographics.csv nbc_showdetails.csv prostate.csv
		Written proposal due
Week 10 (Oct 27)	Unsupervised learning	(Deep) neural netwoks
Week 11 (Nov 3)	Causality	Rescheduled
Week 12 (Nov 10)	Causal inference powered by ML and AI	In-Class Exam 2
Week 13 (Nov 17)	Time Series Forecasting	Social networks
Week 14 (Nov 24)	Numerical optimization	LLM and AI
Week 15 (Dec 1)	Final Presentation	Final Presentation
Week 16 (Dec 8)	Rescheduled	No Class
Final report due on Monday Dec 15

Assignment 1 (Due on Monday Sep 15) .qmd .html	Solution 1 .qmd .html
Assignment 2 (Due on Monday Oct 12) .qmd .html	Solution 2 .qmd .html

Part of the course content related to programming and data science tools is adapted from Grant McDermott's Data Science for Economists course and Zhentao Shi's course with the same title.
Some portions of the course content related to machine learning and causal inference are built upon lecture notes developed by Michael Leung, Tim Armstrong and Roger Moon at USC based on the textbook Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions by Matt Taddy.
Some portions of the course content related to regression models and regularized regression leverage on the lecture notes developed by Jacob Bien at USC for the course DSO 699.
Other key references

R for Data Science by Hadley Wickham and Garrett Grolemund
Advanced R by Hadley Wickham
An Introduction to Statistical Learning (with applications in R) by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
Mostly Harmless Econometrics by Joshua D. Angrist and Jörn-Steffen Pischke
Applied Causal Inference Powered by ML and AI by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis

Python for Data Analysis by Wes McKinney
Coding for Economists by Arthur Turrell
An Introduction to Statistical Learning (with applications in Python) by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani and Jonathan Taylor