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Python for Data Science

John Stachurski

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Creative Commons License – This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International.

A theme by QuantEcon

Contents

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

QuantEcon

Lecture (PDF)
Book (PDF)

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