You are here

Computational Social Science Books

The latest computational social and data science titles from SAGE

As humans increase their use of technology for both business and pleasure, so does the size of
the digital footprint they leave behind. This data is a rich source for study as it provides a unique
perspective on social and human behaviour.

SAGE provides a wide range of resources to help students and researchers manage and make sense of this data, from gentle introductions to data science for those with little or no experience, to more advanced texts using a range of software.

 

 

.Doing Computational Social Science Front Cover

 

 

DOING COMPUTATIONAL SOCIAL SCIENCE
 

John McLevey

This beginner’s guide discusses a range of computational methods and how to use them to study the
problems and questions you want to research. It offers step-by-step guidance for coding in Python and
drawing on examples of real data analysis to demonstrate how you can apply each approach, including
machine learning and social network analysis, in any discipline.

9781526468185 | £36.99 | November 2021 | 768 pages

 

Programming with Python front cover

 

PROGRAMMING WITH PYTHON FOR SOCIAL SCIENTISTS

Phillip D. Brooker

A book that offers a vital foundation and assumes no prior coding knowledge,
it includes:
• 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,
it provides a complete introduction to programming with Python and incorporating it into research design
and analysis.

9781526431721 | £34.99 | December 2019 | 328 pages

 

PROGRAMMING IN PSYCHOPY front cover

 

PROGRAMMING IN PSYCHOPY

Jonathan Peirce

Divided into three parts and with unique learning features to guide readers at whatever level they are at, this
textbook is suitable for teaching practical undergraduate classes on research methods, or as a reference
text for the professional scientist.
The book is written by Jonathan Peirce, the original creator of PsychoPy and Michael MacAskill who have
together utilised their breadth of experience in Python development to educate students.

9781473991392 | £36.99 | May 2018 | 312 pages

 

DOING DATA SCIENCE IN R front cover

 

DOING DATA SCIENCE IN R: An Introduction for Social Scientists
 

Mark Andrews

This approachable introduction to doing data science in R provides step-by-step advice on using the tools
and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R
before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it
builds knowledge and skills gradually.

9781526486776 | £36.99 | March 2021 | 640 pages

 

GEOGRAPHICAL DATA SCIENCE AND SPATIAL DATA ANALYSIS front cover

 

GEOGRAPHICAL DATA SCIENCE AND SPATIAL DATA ANALYSIS

Lex Comber, Chris Brunsdon

We are in an age of big data where all of our everyday interactions and transactions generate data. Much
of this data is spatial – it is collected some-where – and identifying analytical insight from trends and
patterns in these increasing rich digital footprints presents a number of challenges.
Whilst other books describe different flavours of Data Analytics in R and other programming languages,
there are none that consider Spatial Data (i.e. the location attached to data), or that consider issues of
inference, linking Big Data, Geography, GIS, Mapping and Spatial Analytics.

9781526449368 | £34.99 | December 2020 | 340 pages

 

QUANTITATIVE SOCIAL SCIENCE DATA WITH R front cover

 

QUANTITATIVE SOCIAL SCIENCE DATA WITH R

Brian J. Fogarty

Relevant, engaging, and packed with student-focused learning features, this book provides the step-bystep
introduction to quantitative research and data every student needs.
Gradually introducing applied statistics and R, it uses examples from across the social sciences to show
you how to apply abstract statistical and methodological principles to your own work.

9781526411501 | £34.99 | November 2018 | 328 pages

 

Front cover of Analyzing Social Networks textbook

 

ANALYZING SOCIAL NETWORKS

Stephen P. Borgatti, Martin, G Everett, Jeffrey C. Johnson

Designed to walk beginners through core aspects of collecting, visualizing, analyzing, and interpreting
social network data, this book uses simple language and equations to provide expert, clear insight into
every step of the research process—including basic maths principles. With a particular focus on NetDraw
and UCINET, the book introduces relevant software tools step-by-step in an easy to follow way.
Focusing on:
• Digital data and social networks like Twitter
• Statistical models to use in SNA, like QAP and ERGM
• The structure and centrality of networks
• Methods for cohesive subgroups/community detection

9781526404107 - £32.99 - January 2018 - 384 pages

 

Data Science for Business with R front cover

 

DATA SCIENCE FOR BUSINESS WITH R

Jeffrey S. Saltz, Jeffrey M. Stanton

Designed for students with little to no experience in related areas like computer science, this text
focuses on the concepts foundational for students starting a business analytics or data science degree
program. To keep the book practical and applied, the authors feature a running case using a global airline
business’s customer survey dataset to illustrate how to turn data in business decisions, in addition to other
examples. To aid in usability beyond the classroom, the text features full integration of freely-available R
and RStudio® software.

9781544370453 | £66.00 | April 2021 | 424 pages

 

An Introduction to Data Science

 

AN INTRODUCTION TO DATA SCIENCE

Jeffrey S. Saltz, Jeffrey M. Stanton

An easy-to-read, gentle introduction for advanced undergraduate, certificate, and graduate students
coming from a wide range of backgrounds into the world of data science. After introducing the basic
concepts of data science, the book builds on these foundations to explain data science techniques using
the R programming language and RStudio® from the ground up.

9781506377537 | £45.99 | December 2017 | 288 pages