Overview
- 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)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (15 chapters)
Keywords
About this book
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Computational Frameworks for Political and Social Research with Python
Authors: Josh Cutler, Matt Dickenson
Series Title: Textbooks on Political Analysis
DOI: https://doi.org/10.1007/978-3-030-36826-5
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-36825-8Published: 23 April 2020
Softcover ISBN: 978-3-030-36828-9Published: 23 April 2021
eBook ISBN: 978-3-030-36826-5Published: 22 April 2020
Series ISSN: 2522-0373
Series E-ISSN: 2522-0381
Edition Number: 1
Number of Pages: XV, 209
Number of Illustrations: 18 b/w illustrations
Topics: Statistics for Social Sciences, Humanities, Law, Political Science and International Relations, general, Methodology of the Social Sciences