Teaching

Table of Contents

ECON 481

Syllabus

Lectures

Many lectures make use of Wes McKinney’s great book Python for Data Analysis, and I am greatly indebted to him. The Julia lecture makes use of Julia for Data Science by Jose Storopoli, Rik Huijzer and Lazaro Alonso.

  1. Introduction to ECON 481 reveal.js Beamer (Caveat Emptor)1
  2. Introduction to Python and Git reveal.js Beamer (Caveat Emptor)
  3. Numerical Computing in Python reveal.js Beamer (Caveat Emptor)
  4. pandas reveal.js Beamer (Caveat Emptor)
  5. Modeling and Data Visualization reveal.js Beamer (Caveat Emptor)
  6. Web Scraping reveal.js Beamer (Caveat Emptor)
  7. Writing Modules and Testing reveal.js Beamer (Caveat Emptor)
  8. SQL reveal.js Beamer (Caveat Emptor)
  9. R reveal.js
  10. Julia reveal.js

Problem Sets

  1. Problem Set 1 (Tests)
  2. Problem Set 2 (Tests)
  3. Problem Set 3 (Tests)
  4. Problem Set 4 (Tests)
  5. Final Project Proposals
  6. Problem Set 5 (Tests)
  7. Problem Set 6 (Tests)

Data

ECON 487

Lectures

  1. Bandit Algorithms reveal.js

Evaluations

For transparency, all of my teaching evaluations (as an instructor and TA) at the University of Washington can be found on this page.

ECON 510 (Winter 2024)
ECON 487 (Autumn 2023)
ECON 487 (Autumn 2022)
ECON 200 (Spring 2022)
ECON 200 (Winter 2022)
ECON 200 (Autumn 2021)
ECON 200 (Winter 2021)
ECON 200 (Autumn 2020)

  1. I export the files to PDF by convention, but I write the slides intending them to be used in HTML format and don’t check that the PDFs look nice – no formatting guarantees. ↩︎

  2. Courtesy of the Environmental Protection Agency ↩︎

  3. Courtesy of Kaggle User bhavya ↩︎

  4. Courtesy of Yahoo! Finance ↩︎

  5. Courtesy of Baseball Reference ↩︎

  6. Courtesy of MarketWatch ↩︎

  7. Courtesy of Yahoo! Finance ↩︎

  8. Courtesy of ShopGoodwill.com ↩︎

  9. Courtesy of FiveThirtyEight ↩︎