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.
Mục lục
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
Giới thiệu về tác giả
Mark Andrews (Ph D) is Senior Lecturer in the Department of Psychology in Nottingham Trent University. There, he specializes in teaching statistics and data science at all levels from undergraduate to Ph D level. Currently, he is the Chair of the British Psychological Society’s Mathematics, Statistics, and Computing section. Between 2015 and 2018, Dr Andrews was funded by the UK’s Economic and Social Research Council (ESRC) to provide advanced training workshop on Bayesian data analysis to UK based researchers at Ph D level and beyond in the social sciences. Dr Andrews’s background is in computational cognitive science, particularly focused Bayesian models of human cognition. He has a Ph D in Cognitive Science from Cornell University, and was a postdoctoral researcher in the Gatsby Computational Neuroscience Unit in UCL and also in the Department of Psychology in UCL.
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