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Doing Data Science in R
An Introduction for Social Scientists
Taschenbuch von Mark Andrews
Sprache: Englisch

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Beschreibung
This approachable introduction to doing data science in R provides step-by-step advice on using data science 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 approachable introduction to doing data science in R provides step-by-step advice on using data science 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.
Über den Autor

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.

Inhaltsverzeichnis
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
Details
Erscheinungsjahr: 2021
Fachbereich: Allgemeines
Rubrik: Sozialwissenschaften
Medium: Taschenbuch
Seiten: 640
Inhalt: Kartoniert / Broschiert
ISBN-13: 9781526486776
ISBN-10: 1526486776
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Andrews, Mark
Redaktion: Andrews, Mark
Hersteller: SAGE Publishing Ltd
Maße: 244 x 170 x 34 mm
Von/Mit: Mark Andrews
Erscheinungsdatum: 24.03.2021
Gewicht: 1,087 kg
preigu-id: 118974603
Über den Autor

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.

Inhaltsverzeichnis
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
Details
Erscheinungsjahr: 2021
Fachbereich: Allgemeines
Rubrik: Sozialwissenschaften
Medium: Taschenbuch
Seiten: 640
Inhalt: Kartoniert / Broschiert
ISBN-13: 9781526486776
ISBN-10: 1526486776
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Andrews, Mark
Redaktion: Andrews, Mark
Hersteller: SAGE Publishing Ltd
Maße: 244 x 170 x 34 mm
Von/Mit: Mark Andrews
Erscheinungsdatum: 24.03.2021
Gewicht: 1,087 kg
preigu-id: 118974603
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