This page is where readers of my upcoming book will be able to find resources discussed in the book along with added content that will be added post-publication.
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Web Visualization Training Blueprint
They seem to be the same on the surface. After all, in both disciplines you spend most of your time solving problems with programming. Really though, that is where the comparison stops.
Data Analysts and Data Scientists spend much more time just doing research. That means looking at data and also testing theories using research methods that involve asking questions and applying statistical methods. Becoming a real expert in this requires a Master’s Degree education level (either in experience or actual degree) and years of in the seat experience.
Web developers on the other hand spend tons of time on the engineering. They work in the world of glue and plumbing and making things look cool. To really become a great web developer you will have to at least learn the skills taught in a programming boot camp (1 year commitment). And you will spend years mastering your craft and keeping up with the yearly onslaught of new web technologies and tools.
Most people who read this book are going to be data analysts who want to do web visualization or web developers who want to apply data science. It’s possible but unlikely that people will pick up this book without any programming experience.
Regardless, I’ve decided to start building a training blueprint with links to the best and most cost effective sources of training. I’m going to assume that you have no programming background but clearly you do so I would approach this as a checklist and just skip the stuff that you already know.
Web Visualizer Training Blueprint
MORE LINKS TO RESOURCES COMING SOON
Chapter Code and Resources
This is where I will point to the resources that I used in the book. Source code will be hosted by Apress as well and if possible I will simply link out to their repository on Github.
I’ve placed all the source code used in the book in a Github repository that you can clone if you would like. In the links below, you will find links out to source code and other resources broken out by chapter.
Chapter 2 Essential R
Chapter 3 A Deeper Dive Into R
Download the Life Expectancy dataset used in the Chapter Three section on Tidyverse data analysis.