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Quantitative Economic Modeling with Data Science Applications

Course Overview

This is a course introducing the computational and data science tools used in modern economics.

We will apply a programming language (Python) to analyzing these sorts of data and making numerical calculations/simulations of models in economics.

The class will focus on practical experience with economics-focused tools and is not intended as a replacement for Computer Science or Statistics courses.

Class will be held online, using Collaborate Ultra through Canvas.

Course Materials and Communications

All materials will be provided online:

Grading

  • Weekly problem sets: 50%
  • Final projects: 45%
  • Attendance/Participation: 5%

UBC values and policies

UBC provides resources to support student learning and to maintain healthy lifestyles but recognizes that sometimes crises arise and so there are additional resources to access including those for survivors of sexual violence. UBC values respect for the person and ideas of all members of the academic community. Harassment and discrimination are not tolerated nor is suppression of academic freedom. UBC provides appropriate accommodation for students with disabilities and for religious and cultural observances. UBC values academic honesty and students are expected to acknowledge the ideas generated by others and to uphold the highest academic standards in all of their actions. Details of the policies and how to access support are available here (https://senate.ubc.ca/policiesresources-support-student-success)

Topics

We won't be able to cover it all

  1. Python Fundamentals
  • Introduction to Python
  • Basics
  • Collections
  • Control Flow
  • Functions
  1. Scientific Computing and Economics
  • Introduction to Numpy Arrays
  • Introduction to Data Visualization in Python
  • Applied Linear Algebra
  • Randomness
  • Optimization
  1. Introduction to Pandas and Data Wrangling
  • Introduction to Pandas
  • The basics
  • The index
  • Storage formats
  • Data cleaning
  • Reshaping
  • Merging
  • Groupby
  • Time series
  • Introductory Data Visualization
  1. Data Science Case Studies and Tools
  • Regression
    • Linear Regression
    • Lasso Regression
    • Neural Networks
    • Random Forests
  • Classification
    • K-means
    • Classification Trees
    • Support Vector Machines
  • Data Visualization
    • Core visualization principles
    • Maps
  • Miscellaneous
    • Web scraping
    • Fitting probability distributions
    • Natural language processing

Schedule

Tentative schedule subject to changes.