The is a data science course with applications to economic problems. The data operations and analytics will be demonstrated in the R language, and python
will be used as a complement.
After completing this course, students are expected to be fluent in a data science programming language and be able to independently conduct data analysis.
This course is design for students with economics training while are new to heavy duty data tasks. Programming experience is helpful, but not prerequisite.
This repository contains all the slides and the accompanying data and scripts (in the folder data_example).
- [RDS] Wickham, Hadley and Grolemund, Garrett (2016). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media, Inc. (Open access at https://r4ds.had.co.nz/)
- [ISLR] James, Gareth., Witten, Daniela., Hastie, Trevor., & Tibshirani, Robert. (2017). An introduction to statistical learning (Vol. 112, p. 18). New York: springer. (Open access at https://www.statlearning.com/)
This work is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Reflection from Paul Romer on Jupyter notebook and open-source science.
- 2024-1-19: R-intro: beginning--Sec 5
- 2024-1-26:
- R-intro: Sec 5--11
- Wickham and Grolemund: Ch 2: workflow: basics