Doing Data Science in R

An Introduction for Social Scientists
1. Auflage 2021
ISBN: 978-1-5264-8676-9
Verlag: SAGE Publishing Ltd

Buch, Englisch, 640 Seiten, Format (B × H): 175 mm x 250 mm, Gewicht: 1267 g

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.

This book:

- Focuses on providing practical guidance for all aspects, helping readers get to grips with the tools, software, and statistical methods needed to provide the right type and level of analysis their data requires
- Explores the foundations of data science and breaks down the processes involved, focusing on the link between data science and practical social science skills
- Introduces R at the outset and includes extensive worked examples and R code every step of the way, ensuring students see the value of R and its connection to methods while providing hands-on practice in the software
- Provides examples and datasets from different disciplines and locations demonstrate the widespread relevance, possible applications, and impact of data science across the social sciences.

Autoren/Hrsg.

Andrews, Mark

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Forschungsmethodik, Wissenschaftliche Ausstattung

Inhaltsverzeichnis

Chapter 1: Data Analysis And Data Science
Chapter 2: Introduction To R
Chapter 3: Data Wrangling
Chapter 4: Data Visualization
Chapter 5: Exploratory Data Analysis
Chapter 6: Programming In R
Chapter 7: Reproducible Data Analysis
Chapter 8: Statistical Models and Statistical Inference
Chapter 9: Normal Linear Models
Chapter 10: Logistic Regression
Chapter 11: Generalized Linear Models for Count Data
Chapter 12: Multilevel Models
Chapter 13: Nonlinear Regression
Chapter 14: Structural Equation Modelling
Chapter 15: High Performance Computing with R
Chapter 16: Interactive Web Apps with Shiny
Chapter 17: Probabilistic Modelling with Stan

Über Autor(innen)

Andrews, Mark
Mark Andrews is an Associate Professor of Statistical Methods in the Department of Psychology at Nottingham Trent University. He teaches statistics to undergraduate and postgraduate students and is the course leader for the MSc in Behavioural Data Science. He also teaches advanced training courses on statistical methods, data science, and machine learning using R and Python.

Mark has a PhD and MSc in Cognitive Science from Cornell University and was previously a postdoctoral research fellow at University College London, working first in the Gatsby Computational Neuroscience Unit and later in the Division of Psychology and Language Sciences. His research interests include statistical methods in the social and behavioural sciences, computational cognitive science and neuroscience, and the application of mathematical and statistical models to understanding human cognition.

Mark was Chair of the British Psychological Society’s Mathematical, Statistical, and Computing Psychology section and is currently deputy chair of the BPS Statistics and Research Methods Advisory Panel. He is also a committee member of the Royal Statistical Society’s section on teaching statistics. He is the author of “Doing Data Science in R: An Introduction for Social Scientists” (SAGE, 2021).