Previous programming experience and classwork is useful, but not required. We will also talk about the applications of the econometric and statistical learning methods.
We will follow the QuantEcon DataScience textbook
Installing software on your laptop is not mandatory. Instead,
- Go to the QuantEcon DataScience Introduction
- One-time setup: click on the "Settings" icon at the bottom of a lecture and choose the server: ubc.syzygy.ca
- Then click on the "Launch Notebook" to launch any notebook in your computing environment
- See Troubleshooting for how to reset notebooks, etc.
- We strongly suggest creating a GitHub account and signing up for the GitHub Student Developer Pack
You are also encouraged to install Python (preferably Anaconda) on your machines.
If possible, please bring a laptop to class to interactively discuss the material.
- Peifan Wu [email protected]
- Office Hours: Mondays 1pm - 2pm, Iona #013
- TA: Sudipta Ghosh [email protected]
- Office Hours: Thursdays 10am - 11am, Zoom link on Canvas
See Syllabus for more details
Major course sections
- Python Fundamentals
- Scientific Computing and Economics
- Introduction to Pandas and Data Wrangling
- Data Science Case Studies and Tools
Grading: Weekly problem sets: 50%; Final projects: 45%; Attendance/Participation: 5%
The final project is open ended. See previous projects
Lecture and Problem Set Schedule
All problem sets are to be sent as clean, executed .ipynb notebooks on Canvas.