The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book 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 with dplyr, data visualization with ggplot2, algorithm building with caret, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with knitr and R markdown. The book is divided into six parts: R, Data Visualization, Data Wrangling, Statistics with R, Machine Learning, and Productivity Tools. Each part has several chapters meant to be presented as one lecture and includes dozens of exercises distributed across chapters.
Throughout the book, we use motivating case studies. In each case study, we try to realistically mimic a data scientist’s experience. For each of the concepts covered, we start by asking specific questions and answer these through data analysis. We learn the concepts as a means to answer the questions. Examples of the case studies included in the book are:
|US murder rates by state
|Trends in world health and economics
|The impact of vaccines on infectious disease rates
|The financial crisis of 2007-2008
|Reported student heights
|Moneyball: Building a baseball team
|MNIST: Image processing hand-written digits
|Movie recommendation systems
This book is meant to be 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 statistical concepts used to answer the case study questions are only briefly introduced, so a Probability and Statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand all the chapters and complete all the exercises, you will be well-positioned to perform basic data analysis tasks and you will be prepared to learn the more advanced concepts and skills needed to become an expert.
We start by going over the basics of R and the tidyverse. You learn R throughout the book, but in the first part we go over the building blocks needed to keep learning.
The growing availability of informative datasets and software tools has led to increased reliance on data visualizations in many fields. In the second part we demonstrate how to use ggplot2 to generate graphs and describe important data visualization principles.
In the third part we demonstrate the importance of statistics in data analysis by answering case study questions using probability, inference, and regression with R.
The fourth part uses several examples to familiarize the reader with data wrangling. Among the specific skills we learn are web scraping, using regular expressions, and joining and reshaping data tables. We do this using tidyverse tools.
In the fifth part we present several challenges that lead us to introduce machine learning. We learn to use the caret package to build prediction algorithms including K-nearest neighbors and random forests.
In the final part, we provide a brief introduction to the productivity tools we use on a day-to-day basis in data science projects. These are RStudio, UNIX/Linux shell, Git and GitHub, and knitr and R Markdown.
This book focuses on the data analysis aspects of data science. We therefore do not cover aspects related to data management or engineering. Although R programming is an essential part of the book, we do not teach more advanced computer science topics such as data structures, optimization, and algorithm theory. Similarly, we do not cover topics such as web services, interactive graphics, parallel computing, and data streaming processing. The statistical concepts are presented mainly as tools to solve problems and in-depth theoretical descriptions are not included in this book.