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We think a lot of people, like our avatars, want to learn about data and don't know where to start. Existing books in data science and statistics span a wide spectrum. On one side of the spectrum are non-technical books extolling the virtues and promise of data. Some of them are better than others. Even the best ones feel like the modern-day business books. But many of them are written by journalists looking to add drama around the rise of data.

These books describe how specific business problems were solved by looking at a problem through the lens of data. And they might even use words like artificial intelligence, machine learning, and the like. Don't get us wrong, these books create awareness. However, they don't delve deeply into what was done, instead focusing specifically on the problem and the solution at a high level.

On the other side of the spectrum are highly technical books. These hardbound, 500-page tomes are as intimidating physically as the content inside is intimidating mentally.

The far sides of this spectrum have mountains of books. This perpetuates the communication gap—most people either read just the business books or just the technical books. Not both.

Thankfully, the gorge between the two extremes contains a handful of excellent books. Two of our favorites are:

 Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, by Foster Provost and Tom Fawcett (O'Reilly Media, 2013)

 Data Smart: Using Data Science to Transform Information into Insight, by John W. Foreman (Wiley, 2013)

We want to add one more to this list by writing a book you can read casually without a computer or pad of paper nearby. If you enjoy our book, we highly recommend taking the next step by reading one or both of the books listed to solidify your understanding. You won't regret it.

Plus, we love this stuff. If we can convey that to you and motivate you to learn more about data and analytics—and inspire you to want to learn more—we'll consider this book a success.