Beginning Data Science in R : Data Analysis, Visualization, and Modelling for the Data Scientist

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.__Beginning Data Science in R__ details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. **What You Will Learn** * Perform data science and analytics using statistics and the R programming language * Visualize and explore data, including working with large data sets found in big data * Build an R package * Test and check your code * Practice version control * Profile and optimize your code ****Who This Book Is For****Those with some data science or analytics background, but not necessarily experience with the R programming language. Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language. Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language. " Front Matter....Pages i-xxvii Introduction to R Programming....Pages 1-28 Reproducible Analysis....Pages 29-44 Data Manipulation....Pages 45-73 Visualizing Data....Pages 75-111 Working with Large Datasets....Pages 113-124 Supervised Learning....Pages 125-167 Unsupervised Learning....Pages 169-204 More R Programming....Pages 205-231 Advanced R Programming....Pages 233-256 Object Oriented Programming....Pages 257-267 Building an R Package....Pages 269-280 Testing and Package Checking....Pages 281-286 Version Control....Pages 287-301 Profiling and Optimizing....Pages 303-346 Back Matter....Pages 347-352