ECON 21410: Computational Methods in Economics
NOTE: Please see the updated Spring 2019 version of this class here
- Class: Tuesdays and Thursdays, 3:30 - 4:50 PM in SHFE 103
- TA Session: Mondays 7:30-8:30 PM in SHFE 103
- Lecturer: Jeremy Bejarano, email@example.com
- Office Hours: by appointment
- Teaching Assistant: Philip Xinyu Cao, firstname.lastname@example.org
- TA Office Hours: Mondays, 1:45-2:45, in the graduate commons (SHFE 201)
- Website: Canvas will be used for grades. Homeworks and notes will be posted on the course GitHub repo: https://github.com/jmbejara/comp-econ-sp18
There should be about 19 classes and 9 TA sessions (first Monday excluded). This means that we have 28 in-class sessions total before the reading period.
- We will have one midterm. It will take place on Monday, April 30 during the regularly scheduled TA session, 7:30-8:30 PM in SHFE 103.
- Read the lectures ahead of time. The lectures (from, e.g., QuantEcon or the Python Data Science Handbook) have hyperlinks to the appropriate text.
- Solutions to the homework will be posted in a separate GitHub repo, found here: https://github.com/econ-21410-sp18/comp-econ-hw-jbejarano1
- Lectures in Quantitative Economics, by Thomas J. Sargent and John Stachurski (QuantEcon)
- Python Data Science Handbook, by Jake VanderPlas (PDSH)
- Python for Data Analysis, 2nd Edition, by Wes McKinney (PDA)
- Assignments must be turned in before midnight on the day that they are due. This means that you must submit your pull request on GitHub at 11:59pm or earlier on the due date (usually a Monday). Late assignments will not be accepted.
Introduction to Python, Git, and GitHub
- Class 1: Tuesday, March 27
- Introduction and Course Overview
- Review syllabus. Talk about course content.
- Demonstrate how course will be using GitHub. Show how to download assignments. Full Git tutorial will be next class.
- Establish times for TA Sessions and for Python Crash Course
- Go over points from Gentzkow and Shapiro’s Practitioner’s Guide
- QuantEcon: "About Python"
- Include demos. These should include interactive plots, widgets, LaTeX
- Python Basics Pretest (in class assessment)
- I will be holding a few hours of lectures on Saturday to go over “A Whirlwind Tour of Python.” I will help students with the assignment.
- Distribute Assignment 0
- Introduction and Course Overview
- Class 2: Thursday, March 29
- Do Before Class:
- "QuantEcon: “An Introductory Example"
- Git, GitHub, and GitHub Classroom
- Go over how we will be using Git and GitHub in this class.
- Download GitKraken. Do in-class Git assignment
- Demonstrate GitHub README system. Teach basics of Markdown
- Python Crash Course: Saturday, March 31.
- Work through QuantEcon: "Python Essentials"
- Saturday, March 31 from 12-2pm in Saieh 146.
- Class 2a: Monday, April 2
- QuantEcon: "Setting up Your Python Environment"
- Due: Assignment 0
- Distribute Assignment 1
- Practice with Numpy, SciPy, and Matplotlib
- Class 3: Tuesday, April 3
- Class 4: Thursday, April 5
- Class 4a: Monday, April 9
Data Wrangling with Python
- Class 5: Tuesday, April 10
- Introduction to Pandas
- Class 6: Thursday, April 12
- Introduction to Pandas, continued
- In-Class Exercise (occupations notebook)
- PDSH: "Handling Missing Data" (read on your own)
- Class 6a: Monday, April 16
- Go over any questions from HW 2
- PDSH: "Hierarchical Indexing"
- Due Assignment 2
- Distribute Assignment 3
- Data Munging with Pandas
- Class 7: Tuesday, April 17
- Class 8: Thursday, April 19
- Class 8a: Monday, April 23
- PDSH: "Vectorized String Operations"
- Due Assignment 3
- Distribute Assignment 4
- Summarizing and Visualizing Data
- Class 9: Tuesday, April 24
- Class 10: Thursday, April 26
- Data visualization with Python
- Class 10a: Monday, April 30
- In-Class midterm, starts at 7:30. You will need to bring your laptop with you to complete the midterm. The midterm will be similar to the HW assignments.signments.