3 Career & Community

3.1 Build Your Career in Data Science

Not R-specific but a great read!

You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager.

https://www.manning.com/books/build-a-career-in-data-science

3.2 Twitter for R Programmers

Oscar Baruffa, Veerle van Son

The R community is very active on Twitter. You can learn a lot about the language, about new approaches to problems, make friends and even land a job or next contract. It’s a real-time pulse of the R community.What can you gain from becoming active on Twitter? This book will talk about the benefits and it will show you how to use Twitter.

https://www.t4rstats.com

3.3 Twitter for Scientists

Not R-specific, but as many R users are also Scientists this book can really help.

https://t4scientists.com/

3.4 Conversations On Data Science

Roger Peng and Hilary Parker

This book collects many of their discussions from the podcast Not So Standard Deviations and distills them into a readable format.

Pay what you want for the ebook, minimum $0.00

https://leanpub.com/conversationsondatascience

3.5 Executive Data Science

Brian Caffo, Roger D. Peng, and Jeffrey Leek

A Guide to Training and Managing the Best Data Scientists. Learn what you need to know to begin assembling and leading a data science enterprise.

Pay what you want for the PDF, minimum $0.00

https://leanpub.com/eds

3.6 Essays on Data Analysis

Roger Peng

This book draws a complete picture of the data analysis process, filling out many details that are missing from previous presentations. It presents a new perspective on what makes for a successful data analysis and how the quality of data analyses can be judged.

Pay what you want for the ebook, minimum $0.00

https://leanpub.com/dataanalysisessays

3.7 Data Science in a Box

Mine Çetinkaya-Rundel

This book focuses on how to efficiently teach data science to students with little to no background in computing and statistical thinking. The core content of the course focuses on data acquisition and wrangling, exploratory data analysis, data visualization, inference, modelling, and effective communication of results.

This book is free online.

https://datasciencebox.org/