by Alex K Gold
In this book, you’ll learn about DevOps conventions, tools, and practices that can be useful to you as a data scientist. You’ll also learn how to work better with the IT/Admin team at your organization, and even how to do a little server administration of your own if you’re pressed into service.
by John David Smith, Sophie Yang, M. Edward (Ed) Borasky, Jim Tyhurst, Scott Came, Mary Anne Thygesen
Great resource for moving from a standard R developer to incorporating R workflows into enterprise-grade technologies using Docker and Databases.
by Greg Wilson
Years ago, Patrick Burns wrote The R Inferno, a guide to R for those who think they are in hell. Upon first encountering the language after two decades of using Python, I thought Burns was an optimist—after all, hell has rules.
I have since realized that R does too, and that they are no more confusing or contradictory than those of other programming languages. They only appear so because R draws on a tradition unfamiliar to those of us raised with derivatives of C. Counting from one, copying data rather than modifying it, lazy evaluation: to quote the other bard, these are not mad, just differently sane.
Welcome, then, to a universe where the strange will become familiar, and everything familiar, strange. Welcome, thrice welcome, to R.
Reproducible Analytical Pipelines require a range of tools and techniques to implement that can be a challenge to overcome, and this book address some of the common knowledge gaps and hard-to-Google problems that upcoming RAP-pers face.
Created and maintained by Oscar Baruffa
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