Although this book covers conceptual knowledge required for those interested in becoming a hands-on user, it is not a “how-to.” The next step for a would-be practitioner is to engage with reading and training options that guide getting started hands-on. Below are resources that cover the technical how-to as well as the more advanced underlying theory and math.

First-stop resources for business users and hands-on practitioners:

• The Predictive Analytics Guide—articles, industry portals, and other resources: www.pawcon.com/guide
• The Predictive Analytics Times—industry news, technical articles, videos, events, and community: www.predictiveanalyticstimes.com
• This book's website—videos, articles, and other resources: www.thepredictionbook.com

Relatively friendly how-to books that manage to be accessible despite the technical nature of executing on predictive analytics:

• Dean Abbott, Applied Predictive Analytics: Principle and Techniques for the Professional Data Analyst (Wiley, 2014).
• John W. Foreman, Data Smart: Using Data Science to Transform Information into Insight (Wiley, 2013).
• Gordon S. Linoff and Michael J. A. Berry, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (Wiley, 2011).
• Anasse Bari, Mohamed Chaouchi, and Tommy Jung, Predictive Analytics For Dummies (For Dummies, a Wiley Brand, 2014).
• Jeffrey Strickland, Predictive Modeling and Analytics (lulu.com, 2014).
• Vijay Kotu and Bala Deshpande, Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner (Morgan Kaufmann, 2014).
• John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy, Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press, 2015).

Leading foundational textbooks for practitioners and researchers of predictive modeling (technical):

• Robert Nisbet, John Elder, and Gary Miner, Handbook of Statistical Analysis and Data Mining Applications (Academic Press, 2009).
• Tom M. Mitchell, Machine Learning (McGraw-Hill Science/Engineering/Math, 1997).
• Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., corr. 3rd printing, 5th printing (Springer, 2009).

Training options for business users and prospective practitioners of predictive analytics:

1. Note: Although all are how-tos, only some training programs are hands-on.

• Predictive Analytics Applied, the online course instructed by the author. Available on demand at any time: www.businessprediction.com.
• Full-day training workshops alongside the conference Predictive Analytics World. Several international events annually. www.pawcon.com.
• Complete list of degree programs in analytics, data mining, and data science: www.kdnuggets.com/education.
• Sameer Chopra, GVP & Chief Analytics Officer, Orbitz Worldwide, “Sameer Chopra's Hotlist of Training Resources for Predictive Analytics,” Predictive Analytics Times, July 6, 2015. www.predictiveanalyticsworld.com/patimes/hotlist-of-training-resources-for-predictive-analytics0706153.

Conferences for both business users and practitioners of analytics:

• Predictive Analytics World (PAW)—Founded by this book's author, PAW is 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. See www.pawcon.com.
• The Predictive Analytics Times Executive Breakfast—Attendance is free for qualified professionals. See www.PredictiveExecutive.com.
• Text Analytics World—The sister event to PAW covering how to make best use of unstructured data, i.e., the majority of data. See www.tawcon.com.

Leading books for the business user of analytics:

• Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics: The New Science of Winning (Harvard Business School Press, 2007).
• James Taylor, Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics (IBM Press, 2011).
• Richard Boire, Data Mining for Managers: How to Use Data (Big and Small) to Solve Business Challenges (Palgrave Macmillan, 2014).
• Bill Franks, The Analytics Revolution: How to Improve Your Business by Making Analytics Operational in the Big Data Era (Wiley, 2014).

A unique conceptual overview that accessibly introduces the scientific concepts, yet also interests experts with a fresh perspective—namely that machine learning could advance to automatically extract from data all future human knowledge, across all fields of science:

• Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books, 2015).

Broader, lay-reader “pop science” books that provide further industrial and cultural perspectives on analytics and big data in general:

• Patrick Tucker, The Naked Future: What Happens in a World that Anticipates Your Every Move? (Current, 2015).
• Luke Dormehl, The Formula: How Algorithms Solve All Our Problems…and Create More (Perigee Books, 2014).
• Stephen Baker, The Numerati (Mariner Books, 2008).
• Ian Ayres, Super Crunchers: Why Thinking-By-The Numbers is the New Way to Be Smart (Bantam, 2007).
• Christian Rudder, Dataclysm: Who We Are (When We Think No One's Looking) (Crown, 2014).
• Steve Lohr, Data-sim: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else (HarperBusiness, 2015).

Despite the final word of this book's title, Die, nowhere does the book gather all death prediction cases together in one place. For a summary of this surprisingly diverse range of applications, see this article:

• Eric Siegel, PhD, “Deathwatch: Five Reasons Organization Predict When you Will Die,” Predictive Analytics Times, July 15, 2013. www.predictiveanalyticsworld.com/patimes/deathwatch-five-reasons-organizations-predict-when-you-will-die/.