Developing Analytic Talent

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3 Developing Analytic Talent

5 Developing Analytic Talent Becoming a Data Scientist Vincent Granville, Ph.D.

6 Developing Analytic Talent: Becoming a Data Scientist Published by John Wiley & Sons, Inc Crosspoint Boulevard Indianapolis, IN Copyright 2014 by John Wiley & Sons, Inc., Indianapolis, Indiana Published simultaneously in Canada ISBN: ISBN: (ebk) ISBN: (ebk) Manufactured in the United States of America No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) , fax (978) Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) , fax (201) , or online at Limit of Liability/Disclaimer of Warranty: The publisher and the author make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation warranties of fitness for a particular purpose. No warranty may be created or extended by sales or promotional materials. The advice and strategies contained herein may not be suitable for every situation. This work is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If professional assistance is required, the services of a competent professional person should be sought. Neither the publisher nor the author shall be liable for damages arising herefrom. The fact that an organization or Web site is referred to in this work as a citation and/or a potential source of further information does not mean that the author or the publisher endorses the information the organization or website may provide or recommendations it may make. Further, readers should be aware that Internet websites listed in this work may have changed or disappeared between when this work was written and when it is read. For general information on our other products and services please contact our Customer Care Department within the United States at (877) , outside the United States at (317) or fax (317) Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at For more information about Wiley products, visit Library of Congress Control Number: Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/ or its affiliates, in the United States and other countries, and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.

7 For my loving wife, Paris, my wonderful daughter, Capri, and son, Zinal, for their constant support. In loving memory of my father, Roger, who introduced me to mathematics when I was a kid.

9 About the Author Vincent Granville, Ph.D., is a visionary data scientist with 15 years of big data, predictive modeling, digital, and business analytics experience. Vincent is widely recognized as the leading expert in scoring technology, fraud detection, and web traffic optimization and growth. Over the last 10 years, he has worked in real-time credit card fraud detection with Visa, advertising mix optimization with CNET, change point detection with Microsoft, online user experience with Wells Fargo, search intelligence with InfoSpace, automated bidding with ebay, and click fraud detection with major search engines, ad networks, and large advertising clients. Vincent also manages the largest big data and analytics data science group on LinkedIn, with more than 100,000 members. Most recently, Vincent launched Data Science Central, the leading community for big data, business analytics, and data science practitioners. Vincent is a former post-doctorate of Cambridge University and the National Institute of Statistical Sciences. He was among the finalists at the Wharton Business Plan Competition and at the Belgian Mathematical Olympiad. Vincent has published 40 papers in statistical journals and is an invited speaker at international conferences. He also developed a new data mining technology known as hidden decision trees, owns multiple patents, published the first data science book, and raised $6M in start-up funding. Vincent is one of the top 20 big data influencers according to Forbes, and was featured on VentureBeat, MarketWatch, and CNN. Vincent can be reached on vii

10 About the Technical Editor Joni Ngai is a Digital Evangelist who works with senior executives at Fortune 500 companies to develop digital vision and leverage technology and data to intelligently engage customers in today s connected world. She has extensive experience leading agencies and customers to develop new practices across digital, CRM, online media, analytics, and technology development. Joni started her digital consulting career with Razorfish in New York in Since then, she has worked with a number of top digital agencies, such as MRM Worldwide and Havas Digital, across the Asia-Pacific region for many global brands like Intel, Microsoft, and P&G. Joni was appointed as Vice-Chair at I-COM China, an industry- backed global forum in digital measurement, to facilitate participation in standardizing online measurements to help industries grow. Joni graduated from the University of Waterloo majoring in Electrical Engineering with an option in Management Science. She also received an Executive MBA degree from the Kellogg School of Management at Northwestern University and Hong Kong University of Science and Technology. Joni also teaches graduate courses for the Master of Science in New Media program at the Chinese University of Hong Kong.

11 Credits Executive Editor Carol Long Project Editor Christina Haviland Technical Editor Joni Ngai Production Editor Christine Mugnolo Copy Editor San Dee Phillips Editorial Manager Mary Beth Wakefield Freelancer Editorial Manager Rosemarie Graham Associate Director of Marketing David Mayhew Marketing Manager Ashley Zurcher Business Manager Amy Knies Vice President and Executive Group Publisher Richard Swadley Associate Publisher Jim Minatel Project Coordinator, Cover Todd Klemme Compositor Cody Gates, Happenstance Type-O-Rama Proofreaders Shannon Coohill, Word One New York Louise Watson, Word One New York Candace Cunningham Indexer Robert Swanson Cover Designer Ryan Sneed/Wiley ix

13 Acknowledgments I would like to thank Chris Haviland and Carol Long from Wiley for making this book happen; for taking the risk; and for turning a vast amount of valuable but unstructured and scattered text, published online, into a coherent, comprehensive, and useful book. In many ways, this complex process is similar to turning unstructured into structured data, a challenge many data scientists face on a regular basis, with solutions provided in this book. Also, I would like to thank my business partner and co-founder, Tim Matteson, who helped grow Data Science Central to a point where it has become not only the leading data science community, but also a modern, lean start-up focused on delivering value. Finally, I want to thank all the members of our community for their numerous comments and support. Without them, this book would not exist. xi

15 Contents at a Glance Introduction xxi Chapter 1 What Is Data Science? 1 Chapter 2 Big Data Is Different 41 Chapter 3 Becoming a Data Scientist 73 Chapter 4 Data Science Craftsmanship, Part I 109 Chapter 5 Data Science Craftsmanship, Part II 151 Chapter 6 Data Science Application Case Studies 195 Chapter 7 Launching Your New Data Science Career 255 Chapter 8 Data Science Resources 287 Index 299 xiii

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