Data Science for Business: A Slack-Based Book Group

Through a series of discussions that occurred at Marketing Evolution Experience in Las Vegas, over a few beers at a bar in Pittsburgh, over the telephone, and through email and Slack, Dan Haggerty, Jim Gianoglio, and I decided we would jointly work our way through Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking as something of a semi-synchronous book group, and we’re opening up the exercise to anyone who would like to participate.

The Goal

We’re looking to get a better intuition behind the core concepts that undergird the application of data science (Jim’s actually just about to finish a formal degree in that area, but he’s agreed to ride along, anyway) in the context of business -- specifically in the world of digital analytics.

As such, while we could simply read the book independently, we felt like it would be more beneficial to chew on each chapter with our peers in the world of digital analytics to figure out exactly how and where we could apply the ideas and concepts in that space, specifically.

The Idea

Our plan is to tackle approximately one chapter a week -- each participant will read it independently, and then we’ll have a discussion about it using Slack (possibly with a targeted date/time each week, but the way we’re thinking about it, it won’t be a killer if a participant isn’t able to participate at that time).

Is This Book Group for You?

If you’re still reading this post, the answer is probably, “Yes.”

Beyond that, all you will need is:

• An interest in the book
• The means to acquire a copy of the book (hard or soft)
• The ability to commit to spending a couple of hours a week for 2.5-3 months starting in early August
• The ability to use Slack

You should not be concerned if you don’t know Python or R, or if you don’t have a statistics, math, or computer science background. From the preface of the book (available for preview on Amazon):

Data Science for Business is intended for several sorts of readers:

Business people who will be working with data scientists, managing data science-oriented projects, or investing in data science ventures,

Developers who will be implementing data science solutions, and

Aspiring data scientists

This is not a book about algorithms, nor is it a replacement for a book about algorithms. We deliberately avoided an algorithm-centered approach. We believe there is a relatively small set of fundamental concepts or principles that underlie techniques for extracting useful knowledge from data.

Our hope is that the material, while dense, will be approachable for anyone with a strong interest in the topic.

Interested?

If you are -- or may -- be interested, fill in this Google form and we’ll be in touch!