
- 456 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
About this book
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.
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Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Doing Data Science in R by Mark Andrews in PDF and/or ePUB format, as well as other popular books in Social Sciences & Research & Methodology in Psychology. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover
- Half Title
- Acknowledgements
- Title Page
- Copyright Page
- Contents
- About the Author
- Online Resources
- 1 Data Analysis and Data Science
- Part I Fundamentals of Data Analysis and Data Science
- 2 Introduction to R
- 3 Data Wrangling
- 4 Data Visualization
- 5 Exploratory Data Analysis
- 6 Programming in R
- 7 Reproducible Data Analysis
- Part II Statistical Modelling
- 8 Statistical Models and Statistical Inference
- 9 Normal Linear Models
- 10 Logistic Regression
- 11 Generalized Linear Models for Count Data
- 12 Multilevel Models
- 13 Nonlinear Regression
- 14 Structural Equation Modelling
- Part III Advanced or Special Topics in Data Analysis
- 15 High-Performance Computing with R
- 16 Interactive Web Apps with Shiny
- 17 Probabilistic Modelling with Stan
- References
- Index