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Holiday book gift ideas for the analytically minded

The gift-giving season is approaching, and it’s time to start thinking about the quantitatively inclined people on your list. A few years ago, I wrote a post offering a list of books. Many new analytical books have been published since then, and there are some classics worth revisiting. So I wanted to list some more recent books and make sure you know about the recommended reading page of the Analytically Speaking webcast series (now in its third year). Many thought leaders from this webcast series — several of whom are authors — share books they recommend, and we continually update that reading list.

The first three books on my list below are from three of our featured keynotes at Discovery Summit this past September. You can view the plenary talks for David Hand and Michael Schrage to get a sense of what their recent books cover. The recording of the speech by Jonah Berger will air in January.

For those interested in analytical concepts — “listening for the melody” versus making the music:

  • The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day, David J. Hand
  • The Innovator's Hypothesis: How Cheap Experiments Are Worth More than Good Ideas, Michael Schrage
  • Contagious: Why Things Catch On, Jonah Berger
  • How Not to Be Wrong: The Power of Mathematical Thinking, Jordan Ellenberg
  • The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarin..., Sharon Bertsch McGrayne
  • The (Honest) Truth About Dishonesty: How We Lie to Everyone — Especially Ourselves, Dan Ariely
  • The Signal and the Noise: Why So Many Predictions Fail — but Some Don't, Nate Silver
  • Business Transformation: A Roadmap for Maximizing Organizational Insights, Aiman Zeid
  • Willful Ignorance: The Mismeasure of Uncertainty, Herbert Weisberg
  • How to Measure Anything: Finding the Value of Intangibles in Business (3rd Edition), Douglas W. Hubbard
  • Data Science for Business: What you need to know about data mining and data-analytic thinking, Foster Provost and Tom Fawcett
  • Keeping Up with the Quants: Your Guide to Understanding and Using Analytics, Thomas H. Davenport and Jinho Kim
  • Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Eric Siegel
  • For those seeking more recent views on data visualization:

    • Medical Illuminations: Using Evidence, Visualization and Statistical Thinking to Improve Healthcare, Howard Wainer
    • The Visual Organization:  Data Visualization, Big Data, and the Quest for Better Decisions, Phil Simon (he is working on a new book due out Feb. 15)
    • The Functional Art: An introduction to information graphics and visualization (Voices That Matter), Alberto Cairo
    • For big data enthusiasts:

      • Numbersense: How to Use Big Data to Your Advantage, Kaiser Fung
      • Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners, Jared Dean
      • Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, Bart Baesens
      • Big Data, Mining, and Analytics: Components of Strategic Decision Making, Stephen Kudyba
      • Dataclysm: Who We Are (When We Think No One's Looking), Christian Rudder
      • Big Data, Big Innovation: Enabling Competitive Differentiation through Business Analytics, Evan Stubbs
      • Big Data at Work: Dispelling the Myths, Uncovering the Opportunities, Thomas H. Davenport
      • And for those who enjoy books on methods, techniques and more focused topics:

        • Statistics
          •  Elements of Statistical Learning (2nd Edition), Trevor Hastie et al.
          • Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction, Bradley Efron
          • Analysis of Messy Data Volume I:  Designed Experiments, George A. Milliken and Dallas E. Johnson
          • Analysis of Messy Data, Volume II: Nonreplicated Experiments, George A. Milliken and Dallas E. Johnson
          • Analysis of Messy Data, Volume III: Analysis of Covariance, George A. Milliken and Dallas E. Johnson
          • Intro Stats (4th Edition), Richard D. De Veaux et al.
          • Business Statistics (3rd Edition), Norean D. Sharpe et al.
          • Bayesian Data Analysis, (3rd Edition), Andrew Gelman et al.
          • Statistics: Learning from Data, Roxy Peck
          • Statistics for Business: Decision Making and Analysis (2nd Edition), Robert A. Stine and Dean P. Foster
          • Modern Industrial Statistics: with applications in R, MINITAB and JMP, (2nd Edition), Ron Kenett and Shelemyahu Zacks
          • Statistical Intervals: A Guide for Practitioners, Gerald Hahn and William Q. Meeker
          • Discovering Partial Least Squares with JMP, Ian Cox and Marie Goddard
          • Predictive modeling and data mining
            • A Practical Guide to Data Mining for Business and Industry, Andrea Ahlmeyer-Stubbe et al.
            • Fundamentals of Predictive Analytics with JMP, Ron Klimberg and Bruce McCullough
            • Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst, Dean Abbott
            • Predictive Modeling and Analytics, Jeffrey Strickland
            • R for Business Analytics, Ajay Ohri
            • Applied Predictive Modeling, Max Kuhn and Kjell Johnson
            • Design of experiments
              • Design and Analysis of Experiments, Douglas C. Montgomery
              • Design and Analysis of Experiments by Douglas Montgomery: A Supplement for Using JMP, Heath Rushing et al.
              • Optimal Design of Experiments: A Case Study Approach, Peter Goos and Brad Jones
              • Quality and reliability
                • Statistical Thinking: Improving Business Performance, Roger Hoerl and Ron Snee
                • Statistical Methods for Reliability Data, Bill Meeker and Luis Escobar
                • Applied Reliability, Third Edition, Paul A. Tobias and David Trindade
                • Grab bag
                  •  JMP Essentials: An Illustrated Step-by-Step Guide for New Users (2nd Edition), Curt Hinrichs and Chuck Boiler
                  • Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS, Richard Zink
                  • Why We See What We Do Redux: A Wholly Empirical Theory of Vision, Dale Purves and Beau Lotto
                  • The MicroGuide to Process and Decision Modeling in BPMN/DMN: Building More Effective Processes by In..., Tom Debevoise et al.
                  • Brain Rules (Updated and Expanded): 12 Principles for Surviving and Thriving at Work, Home, and Scho..., John Medina
                  • Among the holiday catalogs (arriving since October) was a book catalog with an appropriate plaque: “Life is short. Read fast.”  If you have some books you’d like to suggest, leave me a comment. Happy reading!

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                    Mike Mercer wrote:

                    If you are at all interested in Designed Experiments Peter Goos and Brad Jones book

                    Optimal Design of Experiments: A Case Study Approach is a must read. Read my comments on the Amazon book site, I called it the most important DOE book ever written.

                    Mike Mercer