Previous programming experience and classwork is useful, but not required. But even if you know how to program, you will learn a great deal of interesting applications.
We will follow the QuantEcon DataScience textbook
You will not need to install any software on your local computer. 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
If possible, please bring a laptop to class to interactively discuss the material.
Section 2
- Peifan Wu peifan.wu@ubc.ca
- Office Hours: Mondays 1pm - 2pm, Iona #013
- TA: Kuo Yan yankuo919@gmail.com
- Office Hours: Fridays 1pm - 2pm, Iona #335
Section 3
- Paul Schrimpf schrimpf@mail.ubc.ca
- Office Hours: Wednesdays 6pm, Zoom link available through Canvas
- TA: Josh Catalano jcatal@student.ubc.ca
- Office Hours: Wednesdays 10am - 11am, Zoom link available through 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
All problem sets are to be sent as clean, executed .ipynb notebooks on Canvas.