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 suggested but not mandatory. Instead,
- Go to the QuantEcon DataScience website and navigate to to notebook you want to use
- To import a lecture into UBC JupyterOpen or Syzygy, click the "launch notebook" icon in the top right corner, and enter: https://open.jupyter.ubc.ca (or https://ubc.syzygy.ca) as the private server. Once we get started, it might be easier for you to install JupyterLab on your own computer.
- 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 Anaconda on your machines.
If possible, please bring a laptop to class to interactively discuss the material.
- Philip Solimine philip.solimine@ubc.ca
- Office Hours: Monday and Wednesday, 11:00am-12:00pm, Iona #106
- Section 3 TA: Joshua Catalano jcatal@student.ubc.ca
- Office Hours Fridays 10:00am - 11:00am, IONA #434
- Section 4 TA: Leopoldo Gutierre leopoldo.mig@gmail.com
- Office Hours Fridays 12:00pm - 1:00pm, Iona #434
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: Problem sets: 40%; Midterm: 15%; Final projects: 20%; Final exam: 20%; Attendance/Participation: 5%
The final project is open ended. See previous projects
Lecture and Problem Set Schedule
Read the problem set submission rules before attempting the problem sets. Not following these rules will result in deducted marks.