Introduction to Data Science Data Analysis and Prediction Algorithms with R

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course.

I R 1. Installing R and RStudio 2. Getting Started with R and RStudio 3. R Basics 4. Programming basics 5. The tidyverse 84 II Data Visualization 7. Introduction to data visualization 8. ggplot2 9. Visualizing data distributions 10. Data visualization in practice 11. Data visualization principles 12. Robust summaries III Statistics with R 13. Introduction to Statistics with R 14. Probability 15. Random variables 16. Statistical Inference 17. Statistical models 18. Regression 19. Linear Models IV Data Wrangling 21. Introduction to Data Wrangling 22. Reshaping data 23. Joining tables 24. Web Scraping 25. String Processing 26. Parsing Dates and Times 27. Text mining V Machine Learning 28. Introduction to Machine Learning 29. Smoothing 30. Cross validation 31. The caret package 32. Examples of algorithms 33. Machine learning in practice 34. Large datasets 35. Clustering VI Productivity tools 36. Introduction to productivity tools 37. Accessing the terminal and installing Git 38. Organizing with Unix 39. Git and GitHub 40. Reproducible projects with RStudio and R markdown

Rafael A. Irizarry is professor of data sciences at the Dana-Farber Cancer Institute, professor of biostatistics at Harvard, and a fellow of the American Statistical Association. Dr. Irizarry is an applied statistician and during the last 20 years has worked in diverse areas, including genomics, sound engineering, and public health. He disseminates solutions to data analysis challenges as open source software, tools that are widely downloaded and used. Prof. Irizarry has also developed and taught several data science courses at Harvard as well as popular online courses.