About the Book
This is a practical cookbook with intermediate-advanced recipes for SPSS Modeler data analysts. It is loaded with step-by-step examples explaining the process followed by the experts.If you have had some hands-on experience with IBM SPSS Modeler and now want to go deeper and take more control over your data mining process, this is the guide for you. It is ideal for practitioners who want to break into advanced analytics.
About the Author :
Keith McCormick is the Vice President and General Manager of QueBIT Consulting's
Advanced Analytics team. He brings a wealth of consulting/training experience in statistics,
predictive modeling and analytics, and data mining. For many years, he has worked in the
SPSS community, fi rst as an External Trainer and Consultant for SPSS Inc., then in a similar
role with IBM, and now in his role with an award winning IBM partner. He possesses a BS in
Computer Science and Psychology from Worcester Polytechnic Institute.
He has been using Stats software tools since the early 90s, and has been training since
1997. He has been doing data mining and using IBM SPSS Modeler since its arrival in North
America in the late 90s. He is an expert in IBM's SPSS software suite including IBM SPSS
Statistics, IBM SPSS Modeler (formally Clementine), AMOS, Text Mining, and Classifi cation
Trees. He is active as a moderator and participant in statistics groups online including
LinkedIn's Statistics and Analytics Consultants Group. He also blogs and reviews related
books at KeithMcCormick.com. He enjoys hiking in out of the way places, fi nding unusual
souvenirs while traveling overseas, exotic foods, and old books. Dean Abbott is the President of Abbott Analytics, Inc. in San Diego, California. He has
over two decades experience in applying advanced data mining, data preparation, and
data visualization methods in real-world data intensive problems, including fraud detection,
customer acquisition and retention, digital behavior for web applications and mobile,
customer lifetime value, survey analysis, donation solicitation and planned giving. He has
developed, coded, and evaluated algorithms for use in commercial data mining and pattern
recognition products, including polynomial networks, neural networks, radial basis functions,
and clustering algorithms for multiple software vendors.
He is a seasoned instructor, having taught a wide range of data mining tutorials and
seminars to thousands of attendees, including PAW, KDD, INFORMS, DAMA, 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. He also has taught both applied
and hands-on data mining courses for major software vendors, including IBM SPSS Modeler,
Statsoft STATISTICA, Salford System SPM, SAS Enterprise Miner, IBM PredictiveInsight, Tibco
Spotfi re Miner, KNIME, RapidMiner, and Megaputer Polyanalyst. Meta S. Brown helps organizations use practical data analysis to solve everyday business
problems. A hands-on analyst who has tackled projects with up to $900 million at stake, she
is a recognized expert in cutting-edge business analytics.
She is devoted to educating the business community on effective use of statistics, data
mining, and text mining. A sought-after analytics speaker, she has conducted over 4000 hours
of seminars, attracting audiences across North America, Europe, and South America. Her
articles appear frequently on All Analytics, Smart Data Collective, and other publications. She
is also co-author of Big Data, Mining and Analytics: Key Components for Strategic Decisions
(forthcoming from CRC Press, Editor: Stephan Kudyba).
She holds a Master of Science in Nuclear Engineering from the Massachusetts Institute of
Technology, a Bachelor of Science in Mathematics from Rutgers University, and professional
certifi cations from the American Society for Quality and National Association for Healthcare
Quality. She has served on the faculties of Roosevelt University and National-Louis University. Tom Khabaza is an independent consultant in predictive analytics and data mining, and
the Founding Chairman of the Society of Data Miners. He is a data mining veteran of over 20
years and many industries and applications. He has helped to create the IBM Scott R. Mutchler is the Vice President of Advanced Analytics Services at QueBIT Consulting LLC. He had spent the first 17 years of his career building enterprise solutions as a DBA, software developer, and enterprise architect. When Scott discovered his true passion was for advanced analytics, he moved into advanced analytics leadership roles where he was able to drive millions of dollars in incremental revenues and cost savings through the application of advanced analytics to most challenging business problems. His strong IT background turned out to be a huge asset in building integrated advanced analytics solutions. Recently, he was the Predictive Analytics Worldwide Industrial Sector Lead for IBM. In this role, he worked with IBM SPSS clients worldwide. He architected advanced analytic solutions for clients in some of the world's largest retailers and manufacturers. He received his Masters from Virginia Tech in Geology. He stays in Colorado and enjoys an outdoor lifestyle, playing guitar, and travelling.
