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Computational Frameworks for Political and Social Research with Python

  • Textbook
  • © 2020

Overview

Authors:
  1. Josh Cutler

    1. Optum Inc., Minneapolis, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Matt Dickenson

    1. Uber Technologies, Denver, USA

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    You can also search for this author in PubMed   Google Scholar

  • Introduces core concepts of computer scientists to political and social scientists
  • Teaches researchers how to collect data and use large volumes of data available online
  • Demonstrates how to collect data via popular APIs (Twitter, Google Maps)
  • Enables researchers to utilize unstructured data in statistical analyses
  • Request lecturer material: sn.pub/lecturer-material

Part of the book series: Textbooks on Political Analysis (TPA)

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Part I

    1. Front Matter

      Pages 1-1
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    2. Getting Started with Python

      • Josh Cutler, Matt Dickenson
      Pages 3-19

    3. Building Software

      • Josh Cutler, Matt Dickenson
      Pages 21-32

    4. Object-Oriented Programming

      • Josh Cutler, Matt Dickenson
      Pages 33-48

    5. Introduction to Algorithms

      • Josh Cutler, Matt Dickenson
      Pages 49-57

    6. Introduction to Data Structures

      • Josh Cutler, Matt Dickenson
      Pages 59-71

    7. Input, Output, and the Web

      • Josh Cutler, Matt Dickenson
      Pages 73-85

    8. Application Programming Interfaces

      • Josh Cutler, Matt Dickenson
      Pages 87-97

    9. Databases

      • Josh Cutler, Matt Dickenson
      Pages 99-116

    10. NoSQL Databases

      • Josh Cutler, Matt Dickenson
      Pages 117-126

  3. Part II

    1. Front Matter

      Pages 127-127
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    2. Introduction to Machine Learning with Python

      • Josh Cutler, Matt Dickenson
      Pages 129-142

    3. Linear Programming

      • Josh Cutler, Matt Dickenson
      Pages 143-149

    4. Practical Programming

      • Josh Cutler, Matt Dickenson
      Pages 151-163

    5. Case Study: Image Processing

      • Josh Cutler, Matt Dickenson
      Pages 165-189

    6. Case Study: Natural Language Processing

      • Josh Cutler, Matt Dickenson
      Pages 191-204

    7. Conclusion

      • Josh Cutler, Matt Dickenson
      Pages 205-207

  4. Back Matter

    Pages 209-209
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Keywords

About this book

This book is intended to serve as the basis for a first course in Python programming for graduate students in political science and related fields. The book introduces core concepts of software development and computer science such as basic data structures (e.g. arrays, lists, dictionaries, trees, graphs), algorithms (e.g. sorting), and analysis of computational efficiency. It then demonstrates how to apply these concepts to the field of political science by working with structured and unstructured data, querying databases, and interacting with application programming interfaces (APIs). Students will learn how to collect, manipulate, and exploit large volumes of available data and apply them to political and social research questions. They will also learn best practices from the field of software development such as version control and object-oriented programming. Instructors will be supplied with in-class example code, suggested homework assignments (with solutions), and material for practical lab sessions.

Authors and Affiliations

  • Optum Inc., Minneapolis, USA

    Josh Cutler

  • Uber Technologies, Denver, USA

    Matt Dickenson

About the authors

Josh W. Cutler began his career commercializing research at Microsoft Live Labs from 2005 to 2009. He holds a BS degree in computer science and math from UW-Madison and later pursued a PhD at Duke University, where he built predictive models analyzing conflict. He has served in leadership roles at multiple data-focused startups, and founded and led a company to acquisition. He currently leads the AI Platforms and Transformation team at Optum.

Matt Dickenson is a senior software engineer at Uber, applying machine learning to transportation. He holds a BS degree in political science from the University of Houston and an MS degree in computer science from Duke University. He has taught introductory programming and data science courses and workshops at Duke University, Washington University in St. Louis, and the University of Miami.

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