In Applied Predictive Analytics, Dean Abbott describes the predictive modeling process from the perspective of a practitioner rather than a theoretician. “Predictive analytics is both science and art…This book describes what I would be telling you if you were looking over my shoulder while I was solving a problem, especially when surprises are occurring in the data.”
Abbott wrote this book for anyone currently in the field of predictive analytics or its related fields, including data mining, statistics, machine learning, data science, and business practices. “This book will also help those who wish to enter these fields but aren’t yet there.”
Moreover, I think the material in this book could be of immense value to those whose direct reports are centrally involved in predictive analytics or its related fields.
Briefly, analytics is the process of using computational methods to discover and report influential patterns in data. “The goal of analytics,” Abbott explains, “is to gain insight and often to affect decisions…There is science behind much of what predictive modelers do, yet there is also plenty of art, where no theory can inform us as to the best action, but experience principles by tradeoffs can be made as solutions are found.”
In other words, analytics reveal patterns that lead to answers to questions that must be answered and/or to solutions of problems that must be solved.
What differentiates predictive analytics from other types of analytics? Abbott responds: “First, predictive analytics is data-driven meaning that algorithms derive key characteristics of the models themselves rather than from assumptions made by the analyst.” They induce models from the data. “Second, predictive analytics algorithms automate the process of finding the patterns from the data. Powerful induction algorithms not only discover coefficients or weights for the models, but also the very form of the models.”
To learn much more about all this, I urge you to read Abbott’s book.
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Dean Abbott is President of Abbott Analytics in San Diego, California. He has over 21 years of experience applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, response modeling, survey analysis, planned giving, predictive toxicology, signal process, and missile guidance. In addition, he has developed and evaluated algorithms for use in commercial data mining and pattern recognition products, including polynomial networks, neural networks, radial basis functions, and clustering algorithms, and has consulted with data mining software companies to provide critiques and assessments of their current features and future enhancements.
Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade to audiences of up to 400, including DAMA, KDD, AAAI, and IEEE conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. Mr. Abbott also has taught both applied and hands-on data mining courses for major software vendors, including Clementine (SPSS, an IBM Company), Affinium Model (Unica Corporation), Statistica (StatSoft, Inc.), S-Plus and Insightful Miner (Insightful Corporation), Enterprise Miner (SAS), Tibco Spotfire Miner (Tibco), and CART (Salford Systems).
His most recent book is Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst, published by John Wiley & Sons (2014).