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2 years ago
Getting Started with Predictive Analytics – an Interview with Eric Siegel

 

Data science and predictive analytics are top of mind – but how do you get started? I had the chance to interview Eric Siegel, founder of Predictive Analytics World, the leading conference series, and author of the popular, award-winning Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which earlier this year released its Revised and Updated edition.

Do you think that people can teach themselves data science?

There’s always a balance between learn-by-doing and more formal educational resources, and it varies by individual. I will say this, though: Even the deepest, most senior, talented expert can at times benefit by learning from others. My favorite example is uplift modeling (aka persuasion modeling – covered by the final chapter of my book, “Predictive Analytics,” or search my name and “uplift modeling white paper” for shorter but similar coverage) – even the brightest, most experienced minds I know often need a non-technical, accessible introduction to what uplift modeling really is before it fully sinks in and they go, “Bingo!”

What was your experience learning data science?

As a grad student through the 90s I loved the abstract idea of machine learning and so I dug in deep in certain areas. As a newbee without much breadth, I reinvented a lot that I later found was standard, which is actually an effective way to learn when you are young and have time. Later as a professor, I taught the machine learning graduate level course (at Columbia), and found how much teaching helps one learn. Finally, in the last thirteen years as an independent consultant, especially chairing the Predictive Analytics World conference series, I gained immensely by hearing from experienced industry practitioners (as well as by doing – does that go without saying?).

What resources would you recommend?

For the best I can do to address that question, here’s a blurb from the new preface to the recently Revised & Updated edition of my book, “Predictive Analytics”:

  • The Hands-On Guide at the end of the book—reading and training options that guide getting started.
  • The book’s website—videos, articles, and more resources: www.thepredictionbook.com
  • Predictive Analytics World—the leading cross-vendor conference series in North America and Europe, which includes advanced training workshop days and the industry-specific events PAW Business, PAW Government, PAW Healthcare, PAW Financial, PAW Workforce, and PAW Manufacturing: www.pawcon.com
  • The Predictive Analytics Guide—articles, industry portals, and other resources: www.pawcon.com/guide
  • Predictive Analytics Applied—the author’s online training workshop. Access immediately, on-demand at any time: www.businessprediction.com

What resources are not as helpful as advertised?

Uh, well practitioners do create great content in this industry (both written and presentations). I would say the most misleading thing in predictive analytics to a newcomer is how many of the pertinent software solution vendors pitch either explicitly or implicitly that a solution can be plug-and-play. What is misleading there is that there is simply no way to get around the heavy data preparation task required before employing any predictive modeling software tool.

Your book “Predictive Analytics” sports hundreds of great ratings on Amazon, but I saw some negative reviews that say it doesn’t show how to actually do the stuff. Is your book good for more technically oriented/savvy readers?

Yes, definitely – but it isn’t a how-to. You need to continue the learning process after my book. As a conceptually complete, substantive introduction and industry overview, practitioners definitely benefit from the book, in addition to newcomers. Allow me another excerpt from its preface:
Although this mathless introduction is understandable by any reader—including those with no technical background—here’s why it also affords value for would-be and established hands-on practitioners:

  • A great place to start—provides prerequisite conceptual knowledge for those who will go on to learn the hands-on practice or will serve in an executive or management role in the deployment of PA.
  • Detailed case studies—explores the real-world deployment of PA by Chase, IBM, HP, Netflix, the NSA, Target, U.S. Bank, and more.
  • A compendium of 182 mini case studies—the Central Tables, divided into nine industry groups, include examples from BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, Match.com, MTV, Pandora, PayPal, Pfizer, Uber, UPS, Wikipedia, and more.
  • Advanced, cutting-edge topics—the last three chapters introduce subfields new even to many senior experts: Ensemble models, IBM Watson’s question answering, and uplift modeling. No matter how experienced you are, starting with a conceptually rich albeit non-technical overview may benefit you more than you’d expect—especially for uplift modeling. The Notes for these three chapters then provide comprehensive references to technically deep sources.
  • Privacy and civil liberties—the second chapter tackles the particular ethical concerns that arise when harnessing PA’s power.
  • Holistic industry overview—the book extends more broadly than a standard technology introduction—all of the above adds up to a survey of the field that sheds light on its societal, commercial, and ethical context.

That said, burgeoning practitioners who wish to jump directly to a more traditional, technically in-depth or hands-on treatment of this topic should consider themselves warned: This is not the book you are seeking (but it makes a good gift; any of your relatives would be able to understand it and learn about your field of interest).

As with introductions to other fields of science and engineering, if you are pursuing a career in the field, this book will set the foundation, yet only whet your appetite for more. At the end of this book, you are guided by the Hands-On Guide on where to go next for the technical how-to and advanced underlying theory and math.

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