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Computational frameworks for politic...

Cutler, Josh.

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Computational frameworks for political and social research with Python

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational frameworks for political and social research with Python/ by Josh Cutler, Matt Dickenson.
Author:	Cutler, Josh.
other author:	Dickenson, Matt.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xv, 209 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Getting Started With Python -- Chapter 2. Building Software -- Chapter 3. Object-Oriented Programming -- Chapter 4. Introduction to Algorithms -- Chapter 5. Introduction to Data Structures -- Chapter 6. Input, Output, and the Web -- Chapter 7. Application Programming Interfaces -- Chapter 8. Databases -- Chapter 9. NoSQL Databases -- Chapter 10. Introduction to Machine Learning with Python -- Chapter 11. Linear Programming -- Chapter 12. Practical Programming -- Chapter 13. Case Study: Image Processing -- Chapter 14. Case Study: Natural Language Processing -- Chapter 15. Conclusion.
Contained By:	Springer eBooks
Subject:	Python (Computer program language) -
Online resource:	https://doi.org/10.1007/978-3-030-36826-5
ISBN:	9783030368265

Computational frameworks for political and social research with Python
Cutler, Josh.

Computational frameworks for political and social research with Python[electronic resource] /by Josh Cutler, Matt Dickenson. - Cham :Springer International Publishing :2020. - xv, 209 p. :ill., digital ;24 cm. - Textbooks on political analysis,2522-0373. - Textbooks on political analysis..

Chapter 1. Getting Started With Python -- Chapter 2. Building Software -- Chapter 3. Object-Oriented Programming -- Chapter 4. Introduction to Algorithms -- Chapter 5. Introduction to Data Structures -- Chapter 6. Input, Output, and the Web -- Chapter 7. Application Programming Interfaces -- Chapter 8. Databases -- Chapter 9. NoSQL Databases -- Chapter 10. Introduction to Machine Learning with Python -- Chapter 11. Linear Programming -- Chapter 12. Practical Programming -- Chapter 13. Case Study: Image Processing -- Chapter 14. Case Study: Natural Language Processing -- Chapter 15. Conclusion.

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.

ISBN: 9783030368265

Standard No.: 10.1007/978-3-030-36826-5doiSubjects--Topical Terms:

729789
Python (Computer program language)

LC Class. No.: QA76.73.P98 / C885 2020

Dewey Class. No.: 005.133

Computational frameworks for political and social research with Python
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245 1 0 $a Computational frameworks for political and social research with Python $h [electronic resource] / $c by Josh Cutler, Matt Dickenson.
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505 0 $a Chapter 1. Getting Started With Python -- Chapter 2. Building Software -- Chapter 3. Object-Oriented Programming -- Chapter 4. Introduction to Algorithms -- Chapter 5. Introduction to Data Structures -- Chapter 6. Input, Output, and the Web -- Chapter 7. Application Programming Interfaces -- Chapter 8. Databases -- Chapter 9. NoSQL Databases -- Chapter 10. Introduction to Machine Learning with Python -- Chapter 11. Linear Programming -- Chapter 12. Practical Programming -- Chapter 13. Case Study: Image Processing -- Chapter 14. Case Study: Natural Language Processing -- Chapter 15. Conclusion.
520 $a 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.
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