Business Knowledge Series Instructors
Dr. Bart Baesens, is an assistant professor at the Department of Decisions Sciences and Information Management of
the K.U. Leuven in Belgium, as well at the School of Management of the University of Southampton in the United Kingdom. His
research focuses on the use of data mining and machine learning techniques for credit scoring and customer relationship
management (CRM) applications. He has published in journals worldwide and has presented at top international conferences.
Bart regularly tutors, advises and provides consulting support to international financial institutions with respect to
their credit risk management and credit scoring policy. He also organizes events, workshops, and conferences on his
research topics.
Mark Bailey, Statistical Training and Technical Services VI, initially began computing on mainframes, but expanded
his academic accomplishments in graduate school at the University of Rochester studying hemoglobin action in blood. He
learned how to program the entire laboratory computer for real-time data acquisition and data reduction. Most of his
computing now is in JMP, SAS, Smalltalk or StarLogo on Windows and Macintosh. Bailey's teaching focus is primarily in the
JMP curriculum: design of experiments and scripting.
Patricia Berglund is a senior research associate in the Survey Methodology Program at the Institute for Social
Research at the University of Michigan. She has extensive experience in the use of SAS and related computing systems for
data analysis and data management. She is currently working in the mental health field using data from the National
Comorbidity Surveys, World Mental Health Surveys, and various other national and international surveys. In addition, she is
involved in development, implementation, and teaching of analysis courses and SAS training programs at the Survey Research
Center/ISR. Patricia holds a B.A. and an MBA from Northwestern University in Chicago, Illinois.
Michael Berry, together with his colleague, Gordon Linoff, has authored some of the most widely read and respected books on data mining. These best sellers in the field have been translated into many languages. Michael is an active practitioner of data mining. His books reflect many years of practical, hands-on experience down in the data mines.
A data mining educator as well as a consultant, Michael is in demand as a keynote speaker and seminar leader in the area of data mining generally and the application of data mining to customer relationship management in particular.
Prior to founding Data Miners in December, 1997, Michael spent 8 years at Thinking Machines Corporation. There he specialized in the application of massively parallel supercomputing techniques to business and marketing applications, including one of the largest database marketing systems of the time.
Michael has both US and UK passports and may legally reside and work in the EU. Michael possesses reading and limited speaking knowledge of French and Spanish. He believes that to understand data, one has to be close to the business that generated it and to the people who make use of it so he is happy to travel to client locations around the world.
Representative engagements include
- building a "best next offer" recommendation system to improve cross selling for a large US bank
- analyzing the performance of competing acquisition channels over time
- performing customer segmentation based on behavioral and demographic variables
- building a model to score the profit potential of any geographic area in the United States for a property and casualty insurer
- scoring customers for attrition risk for a credit card issuer
- analyzing web log data to characterize the differences between registered and unregistered users
- helping data mining software companies improve their products
- providing expert witness testimony and litigation support for law suits involving data mining.
Dr. Oral Capps is a demand and price analyst, with particular expertise in econometric modeling and forecasting methods. He is a nationally and internationally recognized leader in demand analysis, specializing in working with large data bases. Applied research areas include analyses of expenditure patterns of preprepared foods and foods eaten away from home, analyses of health and nutrition issues, uses of scanner-derived information for managerial decision-making in food retailing, and analyses of regional, national, and international markets for the agricultural, agribusiness and financial sectors. In addition, Dr. Capps specializes in unilateral price effects of mergers and acquisitions as well as evaluations of agricultural checkoff programs.
Currently a Full Professor and holder of the Southwest Dairy Marketing Endowed Chair in the Department of Agricultural Economics at Texas A&M University as well as founder and Managing Partner of Forecasting and Business Analytics, LLC, Dr. Capps was educated at Virginia Tech. He earned his B.S. in Mathematics in 1975, M.S. in Agricultural Economics in 1977, with a second M.S. in Statistics, and his Ph.D. in Agricultural Economics in 1979. He has authored 105 refereed journal articles, and co-authored two books, Food Demand Analysis: Implications for Future Consumption and Introduction to Agricultural Economics. Another book, A Step-by-Step Approach to Economic Modeling and Forecasting, is forthcoming. In 1995, he was honored at Texas A&M University with the Association of Former Students' Distinguished Achievement Award for Teaching. In 1997, he was the recipient of the Journal of Food Distribution Research Best Journal Article Award. In 1998, he received recognition via the Vice Chancellor's Award in Excellence for Team Research at Texas A&M University. In 1999, he was the recipient of the American Agricultural Economics Association Distinguished Graduate Teaching Award, and a co-recipient of the Applied Consumer Economics Research Award given by the American Council on Consumer Interests. In 2000, he was the co-recipient of the Agricultural and Resource Economics Review Outstanding Journal Article Award. In 2001, Dr. Capps received the Frank Panyko Distinguished Service Award from the Food Distribution Research Society. In 2002, Dr. Capps was bestowed the Vice Chancellor's Award in Excellence for Research at Texas A&M University. Finally, he received the Association of Former Students Faculty Distinguished Achievement Award for Teaching from Texas A&M University in 2003. In 2004, Capps was named a "Fish Camp" namesake by the Texas A&M University undergraduate students.
Dr. Capps is a survivor of the San Francisco earthquake on October 17, 1989 and the New York City World Trade Center attack on September 11, 2001. Special Focus: econometrics, market analysis for agricultural commodities, applied statistics, and major league baseball.
Patricia B. Cerrito is a senior biostatistician at the Jewish Hospital Center for Advanced Medicine and the
University of Louisville, with numerous collaborations in the use of data mining to examine health outcomes. Recently, she
was the project director for an NSF grant to combine Geographic Information Systems and environmental details with data
mining to investigate the need for treatment for shortness of air in a hospital emergency room. Dr. Cerrito is using SAS
Text Miner to investigate chart notes in patient records. She is collaborating with the SAS developers in the use of SAS
Text Miner.
Dr. Goutam Chakraborty has a B.Tech (Honors) in Mechanical
Engineering from Indian Institute of Technology, Kharagpur, a PGCGM from Indian Institute of Management, Calcutta, a M.S in
statistics and a Ph. D. in marketing from University of Iowa. He has held managerial positions with a subsidiary of Union
Carbide, USA and with a subsidiary of British American Tobacco, UK. He is a professor of marketing at Oklahoma State
University where he has taught data mining and CRM applications, data base marketing, new product development, marketing
research, digital business strategy, web-business strategy, electronic commerce and interactive marketing and product and
pricing management for the past sixteen years. He has presented numerous programs and workshops to executives, educators,
and research professionals in U.S., Europe, Singapore, Hong Kong, Dubai, Abu Dhabi, and India. He has won many teaching
awards including "Regents Distinguished Teaching Award" at OSU, "Outstanding Direct Marketing Educator Award" given by the
Direct Marketing Educational Foundation, New York, Professor of the Year Award at CIMBA Italy, and Great Executive MBA
Instructor Award at the University of Iowa, Iowa City and Outstanding Marketing Teacher Award given by the Academy of
Marketing Science, Coral Gables, Florida. His research has been published in many scholarly journals such as Journal of
Interactive Marketing, Journal of Advertising Research, Journal of Advertising, Journal of Business
Research, Industrial Marketing Management, etc. He co-authored the book Contemporary Database Marketing.
In addition, he serves(d) on the editorial review board of Journal of Business Research and Journal of Academy of
Marketing Science. He has chaired the national conference for direct marketing educators for 2004 and 2005 and
co-chaired the M2007 data mining conference. He has also consulted extensively on issues related to developing digital
business strategy, building and managing customer relationships, product development, and management and creation of
e-business models with companies such as Aetna, Mercruiser, Thrifty Rent-A-car, Berendsen Fluid Power, Globe Life
Insurance, Vanguard Realtors, Hilti, etc. He is also the founder of the SAS/OSU Data Mining Certificate program at
Oklahoma State University.
Ron Cody, Ed.D., is a retired professor from the Robert Wood Johnson Medical School who now works as a private
consultant and a national instructor for SAS Institute Inc. A SAS user since 1977, Ron's extensive knowledge and innovative
style have made him a popular presenter at local, regional, and national SAS conferences. He has authored or co-authored
numerous books, such as Learning SAS by Example: A Programmer's Guide; SAS Programming by Example; Applied
Statistics and the SAS Programming Language, Fifth Edition; The SAS Workbook; The SAS Workbook Solutions;
Cody's Data Cleaning Techniques Using SAS Software; Longitudinal Data and SAS: A Programmer's Guide; and
SAS Functions by Example, as well as countless articles in medical and scientific journals.
David Dickey is currently a professor in the Department of Statistics at North Carolina State University. He has a
master's degree in Math from Miami University in Oxford, Ohio, and a Ph.D. in Statistics from Iowa State University. His
research focuses on time series analysis, dealing with data taken over time. In addition to writing four books, Dr. Dickey
has been published in numerous papers and has given over 50 presentations at a variety of professional events and
organizations. He has also been recognized as a member of the Academy of Outstanding Teachers at North Carolina State
University.
Craig Dickstein, an independent Consultant, works with clients and select project teams to implement customized
business solutions for the healthcare industry. He has significant past experience managing and developing SAS
applications and has been a SAS user since 1978. With a Masters Degree in Statistics and meaningful work experience in
medical research, health insurance, and national catalogue sales, he brings benefit by resolving specific client business
needs. Craig has a long history of SAS user group involvement as both an organizer and invited speaker.
Richard J. Fox, Ph.D., is an associate professor of marketing in the Terry College of Business at the University of
Georgia. After receiving his Ph.D., Dr. Fox joined Procter & Gamble in Cincinnati, Ohio, where he served as an
internal statistical consultant. His primary responsibility in the latter half of his 10-year career at Procter &
Gamble was to manage consumer research teams in various corporate divisions. Dr. Fox's industry experience also includes
several years as the manager of quantitative research at Kenneth Hollander Associates, Inc., a market research firm in
Atlanta, and a manager of marketing research and information systems at Nimslo Corporation, a start-up photography company.
His teaching responsibilities in the Marketing Department of the Terry College of Business are primarily related to the
Master of Marketing Research program. Dr. Fox has also served as a consultant to numerous companies including Coca-Cola and
Frito-Lay. Most recently, he has been engaged by The Colography Group Inc. to assist in sample design and estimation
issues. He has published in numerous professional journals, including Journal of Marketing, Journal of the
Academy of Marketing Science, Annals of Mathematical Statistics, and Annals of Statistics. His research
interests include choice models, forecasting market potential of new products, and, in general, application of quantitative
methods to business problems.
Gordon Linoff has had a keen interest in understanding and analyzing large data sets and in applying the results to business problems since he was a student at the Massachusetts Institute of Technology. Gordon is a practitioner, thought-leader, and teacher in the area of data mining.
The talk presented here is based on his most recent book, Data Analysis with SQL and Excel, which is published by Wiley and due out at the end of 2007. His earlier books, written with his colleague Michael Berry, include Data Mining Techniques for Marketing, Sales, and Customer Relationship Management and Mastering Data Mining: The Art and Science of Customer Relationship Management.
Gordon is a Founder and Principal of Data Miners, Inc., a consulting group focused on data mining. Prior to founding Data Miners in 1998, he also worked at Naviant Technology Solutions and Thinking Machines Corporation. He has consulted for a wide range of companies including Bank of America, BT, The Limited, The New York Times, T-Mobile, The Teaching Company, and Pfizer.
Dr. Robert M. Lucas, PhD, Director, Statistical Training and Technical Services, SAS Institute, has a PhD in
Statistics from Colorado State University and over 27 years experience as an applied statistician. Dr. Lucas worked for 17
years at Research Triangle Institute applying statistical techniques to collect and analyze data for a broad range of
scientific and business problems. During his tenure at SAS Institute, Dr. Lucas has developed and taught advanced
statistics; time series, data mining, and mathematical optimization classes as well provided customized training or
consulting in many industries including government, pharmaceuticals, banking, manufacturing and retail.
George A. Milliken is currently a professor of statistics at Kansas State University. He received his Ph.D. from
Colorado State University. His research and professional interests focus on linear and nonlinear mixed models, design of
experiments, and appropriate experimental units. Dr. Milliken also does work with generalized mixed models, repeated
measures, and non-replicated experiments. He co-authored the three-volume set Analysis of Messy Data, as well as the
popular book SAS System for Mixed Models. He is a member and fellow of the American Statistical Association,
Biometrics Society (ENAR), and Institute of Statistical Mathematics.
Dr. Christophe Mues is an assistant professor at the School of Management of the University of Southampton (UK). One of his key research interests is in the business intelligence domain, where he has investigated the use of decision table and diagram techniques in a variety of problem contexts, most notably business rule modeling and validation. Two other key research areas are knowledge discovery and data mining, with a strong interest in applying data mining techniques to financial risk management and, in particular, credit scoring. He has cooperated with public services, companies, and financial institutions in each of these areas, and his findings have been published in various journals and presented at international conferences. He has taught training courses on Credit Scoring for Basel II in several European and Asian countries, all in collaboration with SAS.
Mike Patetta is a senior instructor and course developer in the Education Division at SAS. A respected instructor, Mike has taught more than 300 analytical and statistical courses during his tenure at the company. Mike's uncanny ability to relate course material to a customer's business problems makes him one of the division's most requested instructors.
But teaching is not his only passion; Mike is also a prolific course developer, serving as the primary developer for some of the division's most popular courses in the SAS analytics curriculum. Since joining SAS in 1994, Mike has served as the primary author for such courses as Categorical Data Analysis Using Logistic Regression, Longitudinal Data Analysis with Discrete and Continuous Responses, and Survival Analysis Using the Proportional Hazards Model. He co-authored the Predictive Modeling using Logistic Regression course and has written a number of specialty courses including Fitting Poisson Regression Models Using the GENMOD Procedure and Determining Power and Sample Size Using SAS/STAT Software.
When he's not teaching or writing, Mike can be found providing technical advice to a number of SAS' largest customers. During his career at SAS, Mike has served as a consultant for a wide range of industries, including pharmaceutical, market research, healthcare, financial services, and retail. His consultative and mentoring services have helped Fortune 500 companies improve their analytical practice, build better models and implement efficient and standard procedures in their analytical endeavors.
Prior to coming to SAS, Mike was a statistician for NC State's Center for Health Statistics, where he maintained Medicaid databases and assisted medical examiners and the tuberculosis control branch on a variety of statistical projects. As an epidemiologist with the state, he helped supervise public health program consultants and establish a head and spinal cord surveillance system.
Mike's serious side is balanced by time spent with his wife and three young daughters. Favorite hobbies include hiking, swimming, reading, camping and visiting various U.S. National Parks.
Mike holds a Bachelor of Arts degree from Notre Dame University and a Master's degree from the University of North Carolina.
David Salsburg, Ph.D., is a Fellow of the American Statistical
Association. David is the author of The Lady Tasting Tea: How
Statistics Revolutionized Science in the Twentieth Century, The Use
of Restricted Significance Tests in Clinical Research, Statistics
for Toxicologists, and Understanding Randomness. He has taught
at the University of Pennsylvania, Harvard University, and Yale University,
among others.
Dr. Salsburg spent 27 years working at Pfizer Central Research, Pfizer, Inc. He was the first statistician hired by Pfizer Central Research and had the opportunity to work in many areas of research, including pharmacology, toxicology, clinical research, marketing research, and operations research. David worked on 16 products that were brought successfully to market and many that did not succeed. David's publications deal with problems in areas ranging from toxicology, pharmacology, clinical research, theoretical mathematical statistics, and applications of statistics to law. He received the Distinguished Statistician Lifetime Achievement Award from the Pharmaceutical Research and Manufacturers Association.
While at Pfizer, Dr. Salsburg worked closely with senior research management to develop statistical tools that can aid in decision making. He has published papers describing some of that work. In this lecture, he reviews techniques that are available to aid in decision making when the amount of information available is less than desired but decisions have to be made regarding further development of a particular compound. These methods include the use of probable error (a method widely used in the earliest years of statistics that has fallen into disuse), Bayesian approaches that can be applied when the decision maker is not highly sophisticated about probabilities, and methods for improving on 'standard' procedures to increase the power of significance tests.
Rajesh Selukar is a developer of procedures for analyzing time series data at SAS. He has a Ph.D. in Statistics from
the University of North Carolina at Chapel Hill. His recent work has mainly centered on a variety of time series modeling
techniques such as ARIMA, UCM, and State Space models. He has also worked on strategies of automating the usage of these
models in large-scale forecasting projects. On several occasions, he has provided consulting help in developing large-scale
forecasting systems for SAS customers in different industry areas such as retail, manufacturing, and hospitality.
Simon Sheather, Ph.D., is a professor and head of the Statistics Department at Texas A&M University. Simon's
research interests are in the fields of nonparametric and robust statistics. He has extensive consulting experience,
particularly in the application of statistical methods to business situations. In 2001, Simon was named an honorary fellow
of the American Statistical Association. Simon is currently listed on
ISIHighlyCited.com among the top one-half of one percent of all
mathematical scientists, in terms of citations of his published work.
Naeem Siddiqi has 15 years experience in credit risk management, both as a consultant and as a risk manager at financial institutions.
At SAS, Naeem has played a key role in the development of SAS Credit Scoring, and continues to provide worldwide support for this initiative. His responsibilities range from pre-sales support to consultancy for various projects. Naeem is the author of Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring (Wiley and Sons, New York, 2005) and is a frequent speaker on credit risk topics.
Naeem has an Honours Bachelor of Engineering from Imperial College of Science, Technology and Medicine at the University of London, and an MBA from York University in Toronto.
F. Michael Speed, Ph.D., is currently a professor of statistics at Texas A&M University. He holds a Ph.D. in
Statistics from Texas A&M and a master's degree in Mathematics from St. Mary's University of San Antonio. Currently,
Dr. Speed is also the Associate Dean of Technology Mediated Instruction in the College of Science at Texas A&M. He
received the College AFS Teaching Award in 2001, as well as the H.O. Hartley Award in 1999. Dr. Speed has also refereed
papers for Technometrics, American Statistician, and Journal of the American Statistical Society. He
has also served as the associate editor of Communications in Statistics. His current research interests include
analyzing harvest data and fresh water inflows in Galveston Bay as well as designing low cost experiments for analyzing
exhaust emission data.
Jill Tao, a Statistical Services Specialist at SAS in Cary, North Carolina, has master's degrees in statistics and
mathematics from the University of Alabama. Jill has used SAS software (Base, SAS/STAT, SAS/GRAPH, and the macro facility)
since 1993, and she started providing SAS/STAT training for SAS Institute in 2001. She has developed and taught many SAS
courses on regression, analysis of variance, and mixed models.
Peter Westfall is the Paul Whitfield Horn Professor of Statistics and the James Niver Professor of Information Systems and Quantitative Sciences at Texas Tech University.
Dr. Westfall has consulted with various companies and government agencies for 20 years and has published more than 90 articles and three books on statistical theory and practice. He is the lead author of the books Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment (Wiley, 1993), Multiple Comparisons and Multiple Tests Using the SAS System (SAS Books by Users, 1999), and Multiple Comparisons and Multiple Tests Using the SAS System Workbook (SAS Books by Users, 2000).
Dr. Westfall received the "Most Outstanding Applications Paper Award" from the American Statistical Association, developed the SAS/STAT procedure PROC MULTTEST, and won the 2005 Excellence in Continuing Education Award from the American Statistical Association.
The Center for Advanced Analytics and Business Intelligence, of which Dr. Westfall is the Associate Director, was cited for its contributions to grid computing technology. Peter is the current editor of The American Statistician, is a Fellow of the American Statistical Association, and is a Fellow of the American Association for the Advancement of Science.
Terry Woodfield is a statistical services specialist in the Education Division of SAS and served as co-chair for
M2003, SAS' sixth annual data mining conference. Dr. Woodfield has more than 28 years of SAS programming experience and has
provided training mentoring services in the areas of statistical forecasting, predictive modeling, and data mining. At SAS,
Dr. Woodfield has developed courses in statistical forecasting, Web mining, and text mining. He is also active in the
statistics profession, presenting papers at numerous statistical conferences and professional meetings, and he has served
on steering committees in data mining and forecasting. He has helped develop forecasting and predictive modeling solutions
for insurance, energy, and retail companies and has been an expert witness in utility rate-making hearings. Before joining
SAS, Dr. Woodfield was the chief statistician at HNC Software, and other prior experience includes statistical software
development in SAS/ETS Research and Development and university teaching and research.
Werner Wothke is an applied statistician with extensive experience
in software development, software publishing, and training with both small
companies and Fortune 500 companies. Werner currently serves as principal
statistician at the American Institutes for Research in Washington, DC. A
native of Germany, Werner holds a Ph.D. in Methodology of Behavioral
Sciences from the University of Chicago.
Werner sees statistics as an interdisciplinary effort that brings together concepts and modeling approaches from different sciences. The recent advances in statistical applications have been rather dramatic, and today we have the computing power to conduct many more interesting and realistic analyses than we had only 10 or 20 years ago. Werner is interested and experienced in communicating statistical concepts and results to non-statisticians, and in how modern statistical visualization can help. He has taught scores of structural equation modeling workshops throughout the US, Europe, and Japan.
Jeff Zeanah is the President of Z Solutions, Inc. a firm focused on the support of organizations through predictive analytics and exploratory data mining. His primary interests and research concern the problems organizations face to improve their business decisions through data analysis, including predictive analytics and the selling of the results. Jeff has consulted with industry leaders in manufacturing, retail, public health, science, finance, nutrition and utilities. He is an instructor for SAS Institute Inc. A frequent guest lecturer at universities on the topic of applying analytics to business, he serves on the board of the Institute for Business Intelligence at The University of Alabama.
As a recognized expert on neural networks and a broad range of exploratory data mining tools Jeff has authored papers on neural networks, exploratory data mining, and the implementation of those techniques in organizations. He is the developer of exploratory approaches and techniques that have been used worldwide by Fortune 500 companies, independent researchers, government agencies, and over 30 universities worldwide. His approaches have been applied in areas as diverse as improving manufacturing processes, analyzing market research, tasting wines, searching for oil, controlling river flow, sizing electric transformers, and classifying stars.