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Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R (Addison-Wesley Data & Analytics Series)
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About the Author
Michael Freeman is a senior lecturer at the University of Washington Information School, where he teaches courses in data science, interactive data visualization, and web development. Prior to his teaching career, he worked as a data visualization specialist and research fellow at the Institute for Health Metrics and Evaluation. There, he performed quantitative global health research and built a variety of interactive visualization systems to help researchers and the public explore global health trends. Michael is interested in applications of data visualization to social justice, and holds a Master’s in Public Health from the University of Washington. Joel Ross is a senior lecturer at the University of Washington Information School, where he teaches courses in web development, mobile application development, software architecture, and introductory programming. While his primary focus is on teaching, his research interests include games and gamification, pervasive systems, computer science education, and social computing. He has also done research on crowdsourcing systems, human computation, and encouraging environmental sustainability. Joel earned his M.S. and Ph.D. in information and computer sciences from the University of California, Irvine.
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Product details
Series: Addison-Wesley Data & Analytics Series
Paperback: 384 pages
Publisher: Addison-Wesley Professional; 1 edition (December 8, 2018)
Language: English
ISBN-10: 0135133106
ISBN-13: 978-0135133101
Product Dimensions:
7 x 0.5 x 9.2 inches
Shipping Weight: 1.1 pounds (View shipping rates and policies)
Average Customer Review:
5.0 out of 5 stars
2 customer reviews
Amazon Best Sellers Rank:
#247,809 in Books (See Top 100 in Books)
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!!
I recently got this book as a supplement to my learning R in a university system since my professor was not the best teacher. I must say I'm glad I found this book. Unlike many other coding books, it starts at the very beginning such as file management, text editors, the best IDEs, and project set up. After these topics it jumps into the typical computer science stuff like variables, data types, etc. It's hard to stress how nice it is for a book to be comprehensive in its approach to learning a computer programming language. Too many books forget that its a contextual process, and it's crucial to understand everything that goes along with simply just coding scripts and functions.The book goes over how to collect and manipulate data, how to visualize data, and how to build and share your applications. It goes over so many important concepts that I've found other books lack like accessing data from databases and web APIs, and it introduces many of the essential functions used for data analysis too. As someone who was personally interested in making maps with R, there are great examples of that too.The writing is clear and concise, the examples are fantastic, and the snippets of code are extremely helpful. You can tell it was written by someone who is good at and excited by data science. If you're looking for an enjoyable, comprehensive, and practical overview of R definitely get this book.
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