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What exactly is data science? With R for Data Science, you'll gain a clear understanding of this discipline for discovering natural laws in the structure of data. Along the way, you'll learn how to use the versatile R programming language for data analysis.
Whenever you measure the same thing twice, you get two results – as long as you measure precisely enough. This phenomenon creates uncertainty and opportunity. Author Garrett Grolemund, Master Instructor at RStudio, shows you how data science can help you work with the uncertainty and capture the opportunities. You'll learn about:
Data Wrangling – how to manipulate datasets to reveal new information
Data Visualization – how to create graphs and other visualizations
Exploratory Data Analysis – how to find evidence of relationships in your measurements
Modelling – how to derive insights and predictions from your data
Inference – how to avoid being fooled by data analyses that cannot provide foolproof results
Through the course of the book, you'll also learn about the statistical worldview, a way of seeing the world that permits understanding in the face of uncertainty, and simplicity in the face of complexity.
Hadley Wickham is an Assistant Professor and the Dobelman Family Junior Chair in Statistics at Rice University. He is an active member of the R community, has written and contributed to over 30 R packages, and won the John Chambers Award for Statistical Computing for his work developing tools for data reshaping and visualization. His research focuses on how to make data analysis better, faster and easier, with a particular emphasis on the use of visualization to better understand data and models.
Garrett Grolemund is a statistician, teacher and R developer who currently works for RStudio. He sees data analysis as a largely untapped fountain of value for both industry and science. Garrett received his PhD at Rice University in Hadley Wickham's lab, where his research traced the origins of data analysis as a cognitive process and identified how attentional and epistemological concerns guide every data analysis.
