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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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