Doing Data Science in R
An Introduction for Social Scientists
- Mark Andrews - Nottingham Trent University
Quantitative/Statistical Research (General) | Statistical Computing Environments
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.
- 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
This book will be extremely useful for advanced UG’s along with those on PGT courses. It will also be an excellent handbook for PGR students. It’s perfect for those taking their first serious steps into becoming actively involved in research employing tools in R.
Mark Andrews has written a must-read primer for anyone using statistical techniques in their research. From introductory through to advanced techniques, an easy, intuitive and example driven book sure to get you the right answer.
Doing Data science in R: An introduction for Social Scientists is one of the best available books to learn how to conduct serious empirical research via rigorous methods and techniques. The text is illustrated with many examples written in R and Stan, and is ideal either as a textbook or for self-study.
This is a very thorough book, and very clearly states in the introduction that the intended audience is interested master's students but probably PhD / PostDoc level learners. While the Introduction to R and the R analysis is all really relevant to any methods teaching content, it is the mathematical formulae that put it out of scope for the course against which I requested an inspection copy. I have recommended it to all my PhD and PostDoc colleagues, though. A go to book - akin to Serious Stats!
Too high level for our undergraduate students who just have a couple of introductory lectures, would suit students at a postgraduate level with more time focussed on R.
The book is well written and easily accessible. I will recommend it to my Grad students who will be using R.
This is a brilliant book. I will recommend this to my class.