ECO225: Big Data Tools for Economists
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ECO225: Big Data Tools for Economists
1 Course Description
This course explores unstructured data sources such as text files, webpages, social media posts, satellite imagery, and how economists harness these types of data. It offers a prac- tical introduction to creating datasets from these types of sources (for example, via web scrapping), linking data sources, and managing and visualizing these data (for example, via geospatial visualization).
The exercises in the course will require Python programming. Previous experience in this language is helpful but not necessary. Students that have no experience with Python can use this course as a starting point.
1.1 Required Text and Software
All textbooks and learning materials are available online for free. I use a different source for each section. Here are some useful references that we will selectively use in our course.
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Provided lecture notes and Jupyter notebooks Pro Git (Scott Chacon, Ben Straub, 2nd edition) https://git-scm.com/book/en/v2 Introduction to Statistical Learning with Applications in R (James, Witten,Hastie, Tibshirani, 2013) http://faculty.marshall.usc.edu/gareth-james/ISL/ Videos, slides and other material posted on Quercus We will mainly use Python and Jupyter notebook. |
1.2 Prerequisites
The professor cannot change or wave the prerequisites. Please contact the Econ depart- ment undergraduate administrative staff if you have any questions.
Prerequisite: ECO100Y1(67%)/(ECO101H1(63%);
ECO102H1(63%))/ECO105Y1(80%)/ECO100Y5(67%)/(ECO101H5(63%); ECO102H5(63%))/(MGEA02H3 (67%); MGEA06H3 (67%)); MAT133Y1(63%)/(MAT135H1(60%);
MAT136H1(60%))/ MAT137Y1(55%)/MAT157Y1(55%); CSC108H1/CSC148H1 Exclusion: ESC190H
1.3 Online Delivery Requirements
This course will have some online components. The lectures will be a combination of pre-recorded and live synchronous lectures and will be posted on Quercus. Collaboration hours are online.
You need high-speed internet, a PC or laptop.
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Keep a calendar with due dates. All times will be posted in local Toronto time, and confusion over time zones will not be considered an appropriate excuse for missing a deadline. Take-home assignments are due at 7:00 pm Toronto time on the due date unless otherwise stated. |
2 Course Rules
2.1 Email Policy
Before you start writing an email to a member of the course staff:
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Please make sure your question is not already answered in the syllabus or announce- ments on Quercus If this is a coding question: - First, try to Google the error that you get (e.g., copy and paste it into Google). Since Python is an open-source program, most of your questions have already been answered on the web. - If you could not fix the issue, post it on our discussion platform. Your class- mates can learn from your questions. We value active participation (asking and answering questions) on our discussion platform. - If you still need more help, attend the collaboration hours and your TAs will answer your questions. - At last, if you tried all of the above and still have a question, send an email to [email protected]
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2022-01-20