These are my lecture notes for the graduate Python course I taught at UC Santa Cruz, one Jupyter notebook per lecture. The notebooks are also on GitHub.
Part I: Python Basics
Following Python Crash Course (Eric Matthes, 3rd ed.), Chapters 1–10.
- Lecture 1: Getting Started, Variables, and Lists
- Lecture 2: Working with Lists and if Statements
- Lecture 3: Dictionaries, User Input, and while Loops
- Lecture 4: Functions
- Lecture 5: Classes, Files, and Exceptions
Part II: Data Analysis, Econometrics, and Machine Learning
- Lecture 6: Numerical Computing and Data Wrangling (NumPy, pandas)
- Lecture 7: Data Visualization (Matplotlib, Seaborn)
- Lecture 8: Econometrics in Python (SciPy, statsmodels, linearmodels, arch)
- Lecture 9: Machine Learning & Deep Learning (scikit-learn, PyTorch)
Data: Kenneth French Data Library (Lectures 6–8) and Yahoo Finance (Lecture 9).