Whilst providing a fundamental understanding of computational social science, this book delves into the tools and techniques used to build familiarity with programming and gain context into how, why and when they are introduced. The overall focus is on helping you understand and design computational social science research, alongside delving into hands-on coding and technical instruction.
Key features include:
- Further reading
- Exercises accompanied by sample code
- Programming examples in Scratch, Python and R
- Key concepts
- Chapter summaries
Table des matières
New kind of social science requires new skills
Computational Thinking, Algorithms and Writing Code
Data Science
Network Analysis
Data Structures
Simulations and Complex Systems
Interactive Systems
Best Practice for Software Development
Computation and Data: Collection, Storage and Manipulation
Computational Social Science
Research Ethics in Computational Social Science
Mistakes and Quality of Results in Computational Social Sciences
Integrating Computational Methods in Research
A propos de l’auteur
Matti Nelimarkka has over a decade of experience in teaching programming: from high school students and freshmen in computer sciences to generic introduction courses for STEM and social science students. He also regularly teaches research method, both traditional and computational to students in STEM and social science, with a special focus on graduate students and advanced computational research methods. His experiences combined with research-based insights on programming education provide him a deep understanding how to make learning fruitful and effective for students.In his scholarly work on computational social science, Dr. Nelimarkka has focused on the essential role of social science theories to structure research formulation and then pinpointing and – when necessarily – developing new tools and approaches to tease out an answer to this question. In addition, his work in the critical algorithm studies gives insights to the challenges of reliability and validity, often not sufficiently addressed in computational social science.This book brings together his experiences across programming and conducting social science has led him to develop an approach where computational thinking and programming are blended with empirical research questions and methods and for higher level questions of doing new kind scholarly work with social science.
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