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Data Mining and Statistical Analysis Using SQL: A Practical Guide for DBAsby Robert P. Trueblood and John N. Lovett Jr.
Synopses & Reviews
This book is not just another theoretical text on statistics or data mining. Instead, it's designed for DBAs, database administrators, who want to buttress their understanding of statistics to support data mining and customer relationship management analytics and who want to use SQL, Structured Query Language. Each chapter is independent and self-contained with examples tailored to business applications. Each analysis technique is expressed in a mathematical format that lends itself to coding either as a database query or as a Visual Basic procedure using SQL.
Each chapter includes: formulas (how to perform the required analysis, numerical example using data from a database, data visualization and presentation options (graphs, charts, tables), SQL procedures for extracting the desired results, and data mining techniques.
About the authors:
Robert P. Trueblood is an analyst at QuantiTech, Inc. He has a Ph.D. in computer science and applications and taught at the university level for 18 years before going into private industry. He enjoys designing and implementing systems in Visual Basic.
John N. Lovett, Jr. is a senior engineering consultant at QuantiTech, Inc. and co-owner, with his anthropologist/archaeologist wife Jane, of Falls Mill and Museum in Belvedere, TN. He has a Ph.D. in industrial engineering, with a BS in mathematics and MS in operations research.
This book is not just another theoretical text about statistics or data mining. No, instead it is aimed for you DBAs who want to use SQL, or bolster your understanding of statistics to support data mining and customer relationship management analytics.
Each chapter is self-contained, with examples tailored to real business applications. And each analysis technique will be expressed in a mathematical format for coding as either a database query or a VB procedure using SQL. Chapter contents include formulas, graphs, charts, tables, data mining techniques, and more
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