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Course Syllabus

Econ 294A, Spring 2024

This course aims to offer students a comprehensive introduction to Python, with a special emphasis on its applications within Economics and Data Science. Our objective is to build foundational programming skills and cover a broad selection of Python packages, equipping students with prior exposure to leverage Python for a wide range of applications.

Administrative Information

  • Main Instructor: Pedro Vallocci
  • Office Hours: Wednesday, 2:30PM - 4:30PM at E2-405C
  • Contact: [email protected]
  • Lecture: Section 1: Tuesdays, 5:20 PM - 6:55 PM @ Crown Clrm 105;

Section 2: Thursdays, 5:20 PM - 6:55 PM @ Crown Clrm 104

  • Course Website: Canvas

Optional references

Several references on Python usually focus on specific packages or use cases. The main benefit of checking a book or a package documentation link when in doubt, as a complement to Stack Overflow and ChatGPT, is serendipity -- you'll probably learn more than what you were looking for.

You don't need to buy any of the following books. They will cover a wider range of topics than we can cover in class.

  • Matthes, Eric. Python Crash Course. 3rd edition.

  • McKinney, Wes. Python for Data Analysis. 2nd edition.

  • Ramalho, Luciano. Fluent Python. 2nd edition.

  • G'{e}ron, Aur'{e}lien. Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow. 3rd edition.

The official documentation links for some common packages are below. Python is a very well-documented language. If you don't understand the logic of a function, checking its documentation from the source will be useful. You'll also stumble upon new functions from the same package that may help you in future coding -- rather than reinventing the wheel, you can find a pythonic way of elegantly coding what you want.

Especially for base Python, Numpy, and Pandas, I recommend having printed cheat sheets close by while coding, as they will help you remember common function names more easily. Check Canvas for the added files.

Tentative Schedule

  • Lecture 1: Introduction to Python

    • Why Python?
    • Data structures in Python
    • Basic commands
    • Loops and conditional statements

  • Lecture 2: Loading Datasets: Pandas and Numpy

    • Introduction to Pandas
    • Data import and export using Pandas
    • Matrix/Linear algebra using Numpy
    • Combining Pandas and Numpy

  • Lecture 3: Data Visualization and Data Analysis

    • Interacting with Web APIs
    • Working with Pandas plot, Matplotlib, and Seaborn

  • Lecture 4: Statsmodels

  • Lecture 5: Working with Panel Data and Time Series

    • Statsmodels
    • Linearmodels
    • Prophet

  • Lecture 6: Version Control

    • Git
    • Uploading your portfolio to GitHub

  • Lecture 7: Web Scraping and Text Mining Using Python

    • Basic web scraping
    • Text mining in Python

  • Lecture 8: Advanced Web Scraping

    • BeautifulSoup, Selenium, and Scrapy

  • Lecture 9: Natural Language Processing

    • SpaCy
    • Gensim

  • Lecture 10: Introduction to Scikit-Learn and TensorFlow

    • Regression analysis
    • Machine Learning
    • Extensions
    • Ensemble Learning