Python for Data Science

Lecture notes for Python for Data Science (DSC1030E / DSC6031E) at the National Graduate Institute for Policy Studies, Winter 2027.

Note

These notes are under construction.

Each lecture can be downloaded as a Jupyter notebook. To use a notebook without installing anything, open Google Colab and choose File, then Upload notebook.

These notes are adapted from two QuantEcon lecture series by Thomas J. Sargent and John Stachurski: A First Course in Quantitative Economics with Python and Python Programming for Economics and Finance. They are licensed under CC BY-SA 4.0.

Getting Started

• 1. Introduction to the Course
• 2. Reading Python and Directing AI
• 3. The Toolkit: What Is Possible
• 4. Working with Data

Distributions and Estimation

• 5. Probability Distributions
• 6. Observed Distributions and Matching Them to Data
• 7. Bivariate Distributions, Correlation and Simple Regression
• 8. Maximum Likelihood Estimation
• 9. Bayesian Estimation and Uncertainty

Prediction

• 10. Prediction
• 11. Classification, Clustering and Forecasting

Inequality, Mobility and Networks

• 12. Heavy Tails and Inequality
• 13. Markov Chains and Social Mobility
• 14. Networks
• 15. Application: A Wealth Tax

Other

• References