If you want nice and tidy package of all the things you need to know to get ready to do great Data Science, then this is the book for you!Programming Skills for Data Science starts at the beginning of the DS journey. It takes you through the basics, careful to ensure that no tricky acronym or software 'gotcha' is left unexplained. Folks will appreciate the book's guiding hand which provides a consistent and carefully thought out introduction to all the tools and technologies you need to know. It starts with command line tools and git, making sure the reader is prepared for the more advanced stuff later in the book. I liked the inclusion on a chapter about using Markdown for documentation purposes. If you already are familiar with this markup language, its an easy thing to skim. But if you haven't used Markdown before, it is great to have it here, right in-line with the other prerequisites.The next section of the book provides a great introduction to R - a powerful and wildly popular tool for 'doing' data science. The way Mike Freeman and Joel Ross build up from simple programming concepts to advanced R features really showcase that they've been teaching this stuff for a long time - and have a system that works! I love the walkthrough of basic data types in R (which are kinda weird even if you are familiar with data types from other languages). I also appreciate the section on where one can find help. Thats one of the biggest lessons to learn when doing any programming work - its completely ok to search out answers when you get stuck. Mike and Joel provide a comprehensive list for doing just that.The main section of the book gets people comfortable with the primary tasks of a Data Scientist - wrangling and visualizing data using code. And here, Joel and Mike again show their expertise by picking the best-in-class packages for working with data in R today. Their showcasing of the 'tidyverse' of packages gets you parsing and working with data in the most direct and powerful way possible. They show you how to get started with ggplot2 for visualizing data quickly. I thought it was nice they include a quick description of the 'grammar of graphics' which is the conceptual framework ggplot2 is built from. There is even a section on making maps in R - using the same tools!The final section of the book on building and publishing data-driven reports and analyses really ties everything together. Their suggestions for building and publishing static and interactive analyses are really some of the best ways to get your work out there. I learned a lot of how to build out these interactive tools using Shiny - and make them look good too!Throughout the book I loved the writing style and the attention to detail. There are innumerable call-outs, tips, and warnings as you read. I love that they provide both Windows and Mac examples of setup and screenshots. And I love that the book comes in full color! So those graphics are easy to read and understand. I think a critique that could be leveled against this book is "well can't you find this all on the Internet?". But really, what book couldn't you say that about, these days? But it is true - there are resources out there that cover chunks of this material. You could probably amass a body of work that hit roughly the same topics. But a benefit of this book is having a single resource where all of this material is packaged for you in one handy-dandy guide, so you don't have to be constantly googling mystery words and trying to piece disparate narratives together while learning something new.This book is a great guide and a great resource  for starting down the Data Science path. I have a copy and will be getting one for my friends and associates that are interested in getting started with working and analyzing data!!