Data Mining And Statistical Analysis Using Sql

Par : John-N Lovett, Robert-P Trueblood

Formats :

    • Nombre de pages410
    • PrésentationBroché
    • Poids0.865 kg
    • Dimensions18,7 cm × 23,5 cm × 2,8 cm
    • ISBN1-893115-54-2
    • EAN9781893115545
    • Date de parution14/09/2001
    • ÉditeurApress

    Résumé

    This book isn't designed to be just another theoretical text on statistics or data mining. Instead, it's aimed at DBAs (database administrators) who want to buttress their understanding of statistics to support data mining, customer relationship management (CRM), and analytics, who want to tackle stats using SQL (Structured Query Language). Each chapter is independent and self-contained with examples that are 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 that illustrate the required analysis, One or more numerical examples using data from a "real world" database, Data visualization and presentation options (graphs, charts, tables), SQL procedures for extracting the desired results, Data mining techniques, Sample database. Four appendixes include a brief overview of relational databases and SQL, a set of all statistical tables referenced in the book, an extensive table of information on various statistical distributions, and Visual Basic routines for performing statistical calculations.
    This book isn't designed to be just another theoretical text on statistics or data mining. Instead, it's aimed at DBAs (database administrators) who want to buttress their understanding of statistics to support data mining, customer relationship management (CRM), and analytics, who want to tackle stats using SQL (Structured Query Language). Each chapter is independent and self-contained with examples that are 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 that illustrate the required analysis, One or more numerical examples using data from a "real world" database, Data visualization and presentation options (graphs, charts, tables), SQL procedures for extracting the desired results, Data mining techniques, Sample database. Four appendixes include a brief overview of relational databases and SQL, a set of all statistical tables referenced in the book, an extensive table of information on various statistical distributions, and Visual Basic routines for performing statistical calculations.