Homework assignments are not graded, but the schedule establishes the cadence for completing them that you must follow to stay on track. Dailies will expose those who don’t follow.
Dates and deadlines for exams and project-related activities are hard and non-negotiable:
Syllabus Day + Data Fundamentals (Aug 17-21)
Reading: Healy, ch 2; BUSN 5000 R orientation and getting started guide; Békés and Kézdi, ch 1; Bueno de Mesquita and Fowler, ch 1
IRL: “How Posit helped Redfin move data from spreadsheets to a reproducible data science environment”, Posit Customer Stories.
Homework 1 – Fri, Aug 21
Beginning to Learn (Aug 24-28)
Reading: Békés and Kézdi, chs 2-3
IRL: “What Can Uber Teach Us About the Gender Pay Gap?”, Freakonomics Radio, Episode 317.
pre-Project exercise – Wed, Aug 26
Homework 2 – Fri, Aug 28
Models for Exploration (Aug 31-Sep 4)
Reading: Békés and Kézdi, ch 4; Bueno de Mesquita and Fowler, chs 2, 4
IRL: Stanford Digital Economies Lab: Canaries Dashboard.
Homework 3 – Fri, Sep 4
Making Inferences (Sep 7-11)
NO CLASS – Mon, Sep 7 (Labor Day)
Reading: Békés and Kézdi, chs 5-6; Bueno de Mesquita and Fowler, ch 6
IRL: “The Cybernetic Teammate”, One Useful Thing, 22 Mar 25.
Homework 4 – Fri, Sep 11
Measurement Error, Sample Selection, and Confounding (Sep 14-18)
Reading: Bueno de Mesquita and Fowler, ch 16
IRL: “Using internal CPS data to reevaluate trends in labor-earnings gaps”, Monthly Labor Review, August 2009.
Homework 5 – Fri, Sep 18
The Bayesian Approach + Part I Exam (Sep 21-25)
Reading: Bueno de Mesquita and Fowler, ch 15; “Bayes theorem, the geometry of changing beliefs”, 3Blue1Brown, 22 Dec 2019.
IRL: “Using Bayesian Updating to Improve Decisions under Uncertainty”, California Management Review, 2020.
PART I EXAM – Thu, Sep 24
Regression Fundamentals (Sep 28-Oct 2, Oct 5-9)
Reading: Bueno de Mesquita and Fowler, ch 5; Békés and Kézdi, chs 7-10; Angrist & Pischke, ch 2
IRL: “Anybody Can Win, but Everybody’s Gonna Lose”, Against the Rules, Season 4, Episode 10.
Project Progress Check submission (optional) – Fri,
Oct 2
Homework 6 – Fri, Oct 9
Potential Outcomes and Causal Inference (Oct 12-16)
Reading: Békés and Kézdi, chs 19-20; Bueno de Mesquita and Fowler, chs 3, 9-11; Angrist & Pischke, ch 1
IRL: Dell’Acqua et al., “The Cybernetic Teammate: A Field Experiment on Generative AI and Teamwork”, Organizational Science, Vol 37, No 4, 2026.
Homework 7 – Fri, Oct 16
Regression Discontinuity (Oct 19-23)
Reading: Bueno de Mesquita and Fowler, ch 12; Angrist & Pischke, ch 4
IRL: “Tom Brady, A.D.H.D., and a Really Bad Headache”, Freakonomics M.D., 11 Jul 23. Companion paper: Layton et al., “Attention Deficit–Hyperactivity Disorder and Month of School Enrollment”, NEJM, 2018.
Homework 8 – Fri, Oct 23
Difference in Differences (Oct 26-30)
FALL BREAK – Fri, Oct 30 (No Classes)
Reading: Békés and Kézdi, ch 22; Bueno de Mesquita and Fowler, ch 13; Angrist & Pischke, ch 5
IRL: “Is Facebook Bad for Your Mental Health?”, Freakonomics M.D., 8 Dec 22.
Homework 9 – Mon, Nov 2
Introduction to Machine Learning (Nov 2-6, 9-13, 16-20)
WITHDRAWAL DEADLINE – Wed, Nov 11
Reading: Békés and Kézdi, chs 13-14.
IRL: “In Defense of Zillow’s Besieged Data Scientists”, Medium, 16 Nov 2021.
Project final submission – Fri, Nov 13
Homework 10 – Fri, Nov 20
THANKSGIVING BREAK – Nov 23-27 (No Classes)
Part II Exam (Nov 30-Dec 1)
PART II EXAM – date and time determined for each section by UGA’s final exam schedule