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: pbrazval@ucsc.edu
• 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.

• Numpy: https://numpy.org/doc/stable/index.html
• Pandas: https://pandas.pydata.org/docs/index.html
• Statsmodels: https://www.statsmodels.org/stable/index.html
• Matplotlib: https://matplotlib.org/stable/

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