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Test C2020-012: IBM SPSS Modeler Data Analysis for Business Partners v2



Classroom Course


Course title: Introduction to IBM SPSS Modeler and Data Mining (V16)

Course duration: 2 days

Course number: 0A005G

Abstract: Introduction to IBM SPSS Modeler and Data Mining (V16) is a two day course, that provides an overview of data mining and the fundamentals of using IBM SPSS Modeler. The principles and practice of data mining are illustrated using the CRISP-DM methodology. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling.


Course title: Advanced Data Preparation Using IBM SPSS Modeler (V16)

Course duration: 1 day

Course number: 0A055G

Abstract: Advanced Data Preparation Using IBM SPSS Modeler (V16) covers advanced topics to aid in the preparation of data for a successful data mining project. You will learn how to use functions, deal with missing values, use advanced field operations, handle sequence data, apply advanced sampling methods, and improve efficiency.


Self-Paced Virtual Classroom

Introduction to IBM SPSS Modeler and Data Mining (V16) is a two day course that provides an overview of data mining and the fundamentals of using IBM SPSS Modeler. The principles and practice of data mining are illustrated using the CRISP-DM methodology. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling.

This is the self paced training version of "Advanced Data Preparation with IBM SPSS Modeler" classroom course. In this course, you'll examine additional topics to aid in the preparation of data for a successful data mining project. You'll learn how to partition records from files, handle missing data, modify fields and create new fields, and work with dates, strings and sequence data.


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