Doing Data Science in R

An Introduction for Social Scientists

Häftad, Engelska, 2021

AvMark Andrews

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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.

Produktinformation

Utgivningsdatum2021-03-31
Mått170 x 242 x 40 mm
Vikt1 090 g
FormatHäftad
SpråkEngelska
Antal sidor640
Upplaga1
FörlagSAGE Publications
ISBN9781526486776

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Mark 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.

Chapter 1: Data Analysis And Data ScienceChapter 2: Introduction To RChapter 3: Data WranglingChapter 4: Data VisualizationChapter 5: Exploratory Data AnalysisChapter 6: Programming In RChapter 7: Reproducible Data AnalysisChapter 8: Statistical Models and Statistical InferenceChapter 9: Normal Linear ModelsChapter 10: Logistic RegressionChapter 11: Generalized Linear Models for Count DataChapter 12: Multilevel ModelsChapter 13: Nonlinear RegressionChapter 14: Structural Equation ModellingChapter 15: High Performance Computing with RChapter 16: Interactive Web Apps with ShinyChapter 17: Probabilistic Modelling with Stan

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