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OverviewThis 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. Full Product DetailsAuthor: Mark AndrewsPublisher: Sage Publications Ltd Imprint: Sage Publications Ltd Weight: 1.090kg ISBN: 9781526486776ISBN 10: 1526486776 Pages: 640 Publication Date: 31 March 2021 Audience: College/higher education , Tertiary & Higher Education Format: Paperback Publisher's Status: Active Availability: In Print This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsChapter 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 StanReviewsThis 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. -- Eugene McSorley 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. -- Jason Hay 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. -- Roula Nezi 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. -- Eugene McSorley 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. -- Jason Hay 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. -- Roula Nezi 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. -- Roula Nezi 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. -- Jason Hay 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. -- Eugene McSorley Author InformationMark Andrews (PhD) 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 PhD 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 PhD 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 PhD 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. Tab Content 6Author Website:Countries AvailableAll regions |