Using Data Science to Transform Information into Insight

3 Facts I Bet You Didn’t Know about Data Science and Scientists:

 

  1. Data scientists are not mystical practitioners of magical arts.
  2. Data scientists are “sexy” according to a recent Harvard Business Review article.
  3. Data science can “call presidential races, reveal more about your buying habits than you’d dare tell your mother, and predict just how many years those chili cheese burritos have been shaving off your life.”

I learned these facts minutes after picking up John Foreman’s new book Data Smart: Using Data Science to Transform Information into Insight.  Data Smart is the textbook for anyone wanting to turn raw data into action that makes a difference.

John is the Chief Data Scientist for MailChimp.com, the email service powering subscriptions marketing campaigns.  MailChimp also powers blogs like this one, allowing you to sign up and receive blog posts in your inbox.  John has also worked with a range of organizations from the FBI and Department of Defense to global corporations including Coca-Cola and Intercontinental Hotels.  You can follow him on Twitter @John4man.

Data Smart by John ForemanJohn, who did you write this book for?

I wrote Data Smart for anyone who wants to learn the cutting edge analytic techniques that businesses like Amazon and Facebook are using to turn their data into revenue.

And when I say “learn,” I don’t mean just “learn about.” In Data Smart, readers use actual techniques, such as artificial intelligence and data mining, to solve real business problems. That way the reader can get a sense of how to apply them to their own work. Think of the book as on-the-job training.

That’s why each chapter works through a data science technique in Excel – spreadsheets are a safe environment that readers feel comfortable working and following along in.

He who would search for pearls must dive below. -John Dryden

I wrote Data Smart for anyone from business intelligence analysts to programmers to quantitative marketers to sports analysts to C suite executives. For anyone who truly wants to learn analytics, this is the most accessible book for gaining a foothold in the discipline.

Misconceptions about Data Science

Give us your definition of data science.  What’s the biggest misconception people have about your field?

Data science is the use of transactional business data (think sales data, website traffic, social interactions, ad conversion data, employee performance data, etc.) to make decisions that result in revenue growth for the business.

There are a few big misconceptions about data science. First, the field isn’t just for those who do online advertising (e.g. Facebook, Twitter, or Google). No, a brick and mortar mom-and-pop shop can benefit from artificial intelligence models too. For instance, if you run a hotel, being able to forecast demand in light of your prices and competitors’ prices is invaluable. And that’s true whether you’re a single hotel or Intercontinental.

Second, you don’t need a Ph.D. to do data science. Some of these techniques, like customer segment detection, are analytically tough, but anyone with the motivation and some spreadsheet skills can learn how to do it.