PPOL564 | Reading List

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Class 1: Reproducibility

• Required Readings
  • The Plain Person’s Guide to Plain Text Social Science - Kieran Healy
    • Ch. 1: Introduction
    • Ch. 2: Keep a Record
    • Ch. 3: Write and Edit
    • Ch. 4: Reproduce Work

Class 2: Version Control

• Required Readings
  • Pro Git - Chacon & Straub
    • Ch. 1: Getting Started
    • Ch. 2: Git Basics

Class 3: Python Notebooks

• Required Readings
  • Jupyter Notebook for Beginners: A tutorial - Pryke, Benjamin. www.dataquest.io
  • Python Data Science Handbook by Jake VanderPlas
    • Help and Documentation in IPython
    • IPython Magic Commands
    • Input and Output History
    • IPython and Shell Commands
• Additional Resources and Suggested Materials
  • More IPython Resources

Class 4: Data Types in Python

• Required Readings
  • Lutz - Ch. 4 (See Canvas)
• Additional Resources and Suggested Materials
  • Mutable vs Immutable Objects in Python
  • Installing Packages using PyPi (i.e. installing packages using the pip shell command)
  • Installing Packages using Anaconda (i.e. installing packages using the conda shell command. Requires having an Anaconda distribution installed.)

Class 5: Control Sequences, Iteration, and Functions

• Required Readings
  • Repeated Actions with Loops - Data Carpentry
  • Making Choices - Data Carpentry
  • Creating Functions - Data Carpentry

Class 6: Comprehensions and Generators

• Required Readings
  • Lutz - Ch.14 (See Canvas)

Class 7: Numpy

• Required Readings
  • Python Data Science Handbook by Jake VanderPlas
    • All of Chapter 2
      • Understanding Data Types in Python
      • The Basics of NumPy Arrays
      • Computation on NumPy Arrays: Universal Functions
      • Aggregations: Min, Max, and Everything In Between
      • Computation on Arrays: Broadcasting
      • Comparisons, Masks, and Boolean Logic
      • Fancy Indexing
      • Sorting Arrays
      • Structured Data: NumPy’s Structured Arrays

Class 8: Data Wrangling with Pandas (part 1)

• Required Readings
  • Python Data Science Handbook by Jake VanderPlas
    • Introducing Pandas Objects
    • Data Indexing and Selection
    • Handling Missing Data
    • Hierarchical Indexing
    • Combining Datasets: Concat and Append
    • Combining Datasets: Merge and Join

Class 9: Data Wrangling with Pandas (part 2)

• Required Readings
  • Python Data Science Handbook by Jake VanderPlas
    • Aggregation and Grouping
    • Pivot Tables
    • High-Performance Pandas: eval() and query()

Class 10: Exploratory Data Analysis

• Required Readings
  • Python Data Science Handbook by Jake VanderPlas
    • Simple Line Plots
    • Simple Scatter Plots
    • Density and Contour Plots
    • Histograms, Binnings, and Density
    • Multiple Subplots
    • Visualization with Seaborn

Class 11 + 12: Vectors + Trigonometry of Vectors

• Required Readings
  • Moore and Siegel
    • Ch. 1, 2, 12.1, 12.3

Class 13 + 14: Matrix Transformations + Matrix Operations and Inversions

• Required Readings
  • Moore and Siegel
    • Ch. 12.3 - 12.6, 13

Class 15: Linear Regression

• Required Readings
  • The Linear Algebra View of Least-Squares Regression - Andrew Chamberlain

Class 16 + 17: Eigen Decompositions

• Required Readings
  • Moore and Siegel
    • Ch. 14.1

Class 18: Differentiation

• Required Readings
  • Moore and Siegel
    • Ch. 3 - 6

Class 19: Optimizing Univariate Functions

• Required Readings
  • Moore and Siegel
    • Ch. 8, 15.1-15.2.2

Class 20: Optimizing Multivariate Functions

• Required Readings
  • Moore and Siegel
    • Ch.15.3-16.1

Class 21: Gradient Descent

• Required Readings
  • Introduction to the Gradient Descent Algorithm - Sara Garcia
  • Gradient Descent - Simply Explained - Koo Shung

Class 22: Constrained Optimization

• Required Readings
  • Moore and Siegel
    • Ch. 16

Class 23: Probability

• Required Readings
  • Moore and Siegel
    • Ch. 9.1 - 9.2.2
    • Ch. 10.1 - 10.6.2, 10.7
    • 11.1 - 11.2.2, 11.3.1 - 11.3.4

Class 24: Bayes Rule + Naive Bayes Algorithm

• Required Readings
  • Moore and Siegel
    • Ch. 9.2.3
  • A Gentle Introduction to Bayes Theorem for Machine Learning - Brownlee, Jason
  • Naive Bayes from Scratch - Brownlee, Jason

Class 25: Simulation and Sampling

• Required Readings
  • McElreath, Richard. Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC, 2018.
    • Ch. 3 (Canvas)
    • Ch. 8.1 - 8.3 (Canvas)