R and Python Training

In each lecture notes, you will be directed to an online resource that covers the subject that we'll be learning. These lecture notes will then provide solutions to the previous problem set and walk you through problems, and eventually real projects that we have embarked on with John List. These lecture notes will be unguided but you can reach out to an RP if you have further questions beyond the solutions. Feel free to go at your own pace. Note that the course outline below is subject to change.

Python Part 1

  1. Expressions and functions

  2. Conditionals and loops

  3. Lists, dictionaries, and tuples

  4. Web scraping with BS4

  5. regex

R Part 1

  1. R Basics

  2. Data cleaning

  3. Aggregates, joins

  4. Reshape, pivot, lubridate

  5. OLS

Python Part 2

  1. Advanced regex

  2. BS4, part 2

  3. Text mining, sentiment analysis

  4. Selenium, part 1

  5. Selenium, part 2

R Part 2

  1. Matrices, logit, IV, 2SLS

  2. Monte Carlo and simulations

  3. Rerandomization and regression adjustments

  4. Mathematical optimization

  5. Machine learning

SQL

  1. SQL

Python Part 3

  1. scipy, machine learning

  2. CUDA, neural networks

  3. Classes, OOP

  4. Functional programming (map, filter, etc.)

  5. Iterators

  6. Exception handling

  7. Threading

  8. numpy, numba

  9. Cython

R Part 3

  1. Classes, functional programming, exception handling, and threading in R

  2. Expression, quasiquotations

  3. Rcpp

Python Part 4

  1. APIs