Programming with Python for Social Scientists
Phillip Brooker
- University of Liverpool, UK
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Go to College Publishing WebsiteDescription
As data become ‘big’, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of – and control over – how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including:
• The fundamentals of why and how to do your own programming in social scientific research
• Questions of ethics and research design
• A clear, easy to follow ‘how-to’ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more.
Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
• The fundamentals of why and how to do your own programming in social scientific research
• Questions of ethics and research design
• A clear, easy to follow ‘how-to’ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more.
Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
Contents
Introduction
Introduction
Chapter 1. What is Programming? And What Could it Mean for Social Science Research?
Chapter 1. What is Programming? And What Could it Mean for Social Science Research?
Chapter 2. Programming-as-Social-Science (Critical Coding
Chapter 2. Programming-as-Social-Science (Critical Coding
Chapter 3. Setting Up to Start Coding
Chapter 3. Setting Up to Start Coding
Chapter 4. Core Concepts/Objects
Chapter 4. Core Concepts/Objects
Chapter 5. Structuring Objects
Chapter 5. Structuring Objects
Chapter 6. Building Better Code with (Slightly) More Complex Concepts/Objects
Chapter 6. Building Better Code with (Slightly) More Complex Concepts/Objects
Chapter 7. Building New Objects with Classes
Chapter 7. Building New Objects with Classes
Chapter 8. Useful Extra Concepts/Practices
Chapter 8. Useful Extra Concepts/Practices
Chapter 9. Designing Research that Features Programming
Chapter 9. Designing Research that Features Programming
Chapter 10. Working with Text Files
Chapter 10. Working with Text Files
Chapter 11. Data Collection: Using Social Media APIs
Chapter 11. Data Collection: Using Social Media APIs
Chapter 12. Data Decoding/Encoding in Popular Formats (CSV, JSON and XML)
Chapter 12. Data Decoding/Encoding in Popular Formats (CSV, JSON and XML)
Chapter 13. Data Collection: Web Scraping
Chapter 13. Data Collection: Web Scraping
Chapter 14. Visualising Data
Chapter 14. Visualising Data
Conclusion: Using Your Programming-as-Social-Science Mindset
Conclusion: Using Your Programming-as-Social-Science Mindset
Description
As data become ‘big’, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of – and control over – how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including:
• The fundamentals of why and how to do your own programming in social scientific research
• Questions of ethics and research design
• A clear, easy to follow ‘how-to’ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more.
Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
• The fundamentals of why and how to do your own programming in social scientific research
• Questions of ethics and research design
• A clear, easy to follow ‘how-to’ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more.
Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
Contents
Introduction
Introduction
Chapter 1. What is Programming? And What Could it Mean for Social Science Research?
Chapter 1. What is Programming? And What Could it Mean for Social Science Research?
Chapter 2. Programming-as-Social-Science (Critical Coding
Chapter 2. Programming-as-Social-Science (Critical Coding
Chapter 3. Setting Up to Start Coding
Chapter 3. Setting Up to Start Coding
Chapter 4. Core Concepts/Objects
Chapter 4. Core Concepts/Objects
Chapter 5. Structuring Objects
Chapter 5. Structuring Objects
Chapter 6. Building Better Code with (Slightly) More Complex Concepts/Objects
Chapter 6. Building Better Code with (Slightly) More Complex Concepts/Objects
Chapter 7. Building New Objects with Classes
Chapter 7. Building New Objects with Classes
Chapter 8. Useful Extra Concepts/Practices
Chapter 8. Useful Extra Concepts/Practices
Chapter 9. Designing Research that Features Programming
Chapter 9. Designing Research that Features Programming
Chapter 10. Working with Text Files
Chapter 10. Working with Text Files
Chapter 11. Data Collection: Using Social Media APIs
Chapter 11. Data Collection: Using Social Media APIs
Chapter 12. Data Decoding/Encoding in Popular Formats (CSV, JSON and XML)
Chapter 12. Data Decoding/Encoding in Popular Formats (CSV, JSON and XML)
Chapter 13. Data Collection: Web Scraping
Chapter 13. Data Collection: Web Scraping
Chapter 14. Visualising Data
Chapter 14. Visualising Data
Conclusion: Using Your Programming-as-Social-Science Mindset
Conclusion: Using Your Programming-as-Social-Science Mindset
Reviews
December 2019 | 328 pages | Sage UK
| Format | Published Date | ISBN | Price |
|---|
As data become ‘big’, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of – and control over – how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including:
• The fundamentals of why and how to do your own programming in social scientific research
• Questions of ethics and research design
• A clear, easy to follow ‘how-to’ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more.
Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
• The fundamentals of why and how to do your own programming in social scientific research
• Questions of ethics and research design
• A clear, easy to follow ‘how-to’ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more.
Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
Table Of Contents:
- Introduction
- Chapter 1. What is Programming? And What Could it Mean for Social Science Research?
- Chapter 2. Programming-as-Social-Science (Critical Coding
- Chapter 3. Setting Up to Start Coding
- Chapter 4. Core Concepts/Objects
- Chapter 5. Structuring Objects
- Chapter 6. Building Better Code with (Slightly) More Complex Concepts/Objects
- Chapter 7. Building New Objects with Classes
- Chapter 8. Useful Extra Concepts/Practices
- Chapter 9. Designing Research that Features Programming
- Chapter 10. Working with Text Files
- Chapter 11. Data Collection: Using Social Media APIs
- Chapter 12. Data Decoding/Encoding in Popular Formats (CSV, JSON and XML)
- Chapter 13. Data Collection: Web Scraping
- Chapter 14. Visualising Data
- Conclusion: Using Your Programming-as-Social-Science Mindset
Recent Product Reviews:
A great resource for social scientists interested in analysing digital data and/or social data.
Dr Nicoleta Gaciu, School of Education, Oxford Brookes University
Recommended to all my students as it supports their social sciences dissertation
Mrs Dawn Burnham, Early Years, Farnborough College of Technology
To introduce students to using Python for social science research.
Dr Uchendu Eugene Chigbu, TUM - Technical University of Munich, TUM - Technical University of Munich
This is an engaging, insightful and sophisticated guide to Python for social scientists. It's a manual of the highest quality and a practice led intervention with the potential to shape the future of the digital social sciences. I can't recommend it highly enough.
Mark Carrigan, University of Cambridge
Great resource for all students and researchers looking for a clear, accessible, yet comprehensive introduction to Python and coding.
Nicola Perra, University of Greenwich