My career has gone through a lot of changes over the past decade (and then some). I wasn’t always this suave debonair analytics guy you see before you. Once upon a time, I was a developer, and for a while I was an Information Architect (that means UX for those of you who weren’t around in the mid 2000s). So my book recommendations may not be exactly what you expect from the title, but maybe that’s what keeps me interesting.
These books are not intended to actually teach any real form of performing data science itself. That’s not something you can learn from any one book (that I know of). In fact, if you check out the chart below, you’ll see that the whole data science thing has a long road to it, that couldn’t possibly be covered with three books.
The top data scientists in the world aren’t successful because of their hardened data skills, or their Malcolm Gladwell “10,000 Hours” of experience. It’s usually because of their diverse view of the world and experience with myriad business problems. This list should provide you with the right mental models, history and otherwise critical thinking skills to help identify real problems in the real world that can be solved (for the most part) with data science.
The Personal MBA (by Josh Kaufmann)
You know those scenes in TV shows where the cops realize they can follow the money to find the criminals?
Well this book is about as close as you can get to learning how to follow the money, or rather, how different businesses think about success. Josh has an extremely friendly writing style, and focuses in on bite sized knowledge, vs. copious amounts of theory. If you’re considering going to business school (which you should if you haven’t), this book is also a great entry point into the types of things you learn in those programs. Ultimately though, this book will provide you with a proper business vocabulary. An essential skill for those who want to navigate through KPIs (Key Performance Indicators).
Envisioning Information (by Edward Tufte)
I wish I had read this book much earlier in my career. Tufte breaks down very complex ideas about data visualization into simple terms, using brilliant real world examples. Not only showing how the masters have done it, but also the mental models used to make these beautiful visuals on your own.
Reading this book can mean the difference between trying to write out very complex ideas vs. visualizing them to an audience for immediate digestion… Which is a brilliant bridging point to the next book:
Universal Principles of Design (by William Lidwell)
The human brain picks up on visual inconsistencies and processes them as incorrect information. Think very hard about this concept. Every time you’ve created a presentation and the colors or fonts or spacing was off, your audience was thinking “Something about this isn’t quite right”. When they should have been thinking “OMG – This is the best information I’ve ever seen”!
Read this book. It will supercharge your presentations, and help you understand the difference between good and bad visualizations.
That’s it for now. Do feel free to add more recommendations in the comments.
This blog post was originally published on Jon Litwack’s personal blog.