Table of Contents
Introduction
Part 1: Programming in Python
About Python
Setting up Your Python Environment
An Introductory Example
Python Essentials
Object Oriented Programming
How it Works: Data, Variables and Names
Advanced Features
Part 2: The Scientific Libraries
NumPy
SciPy
Matplotlib
Pandas
IPython
Part 3: Introductory Applications
Finite Markov Chains
Shortest Paths
A First Look at the Kalman Filter
Schelling’s Segregation Model
LLN and CLT
Infinite Horizon Dynamic Programming
Part 4: Advanced Applications
Continuous State Markov Chains
Modeling Career Choice
On-the-Job Search
Search with Offer Distribution Unknown
Optimal Savings
Linear Stochastic Models
Estimation of Spectra
Optimal Taxation
Solutions to Exercises
Useful Resources
References