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0A005G – Introduction to IBM SPSS Modeler and Data Mining

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

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
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0A007G – Introduction to IBM SPSS Modeler and Data Mining

This course provides an overview of data mining and the fundamentals of using IBM SPSS Modeler (v18). 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.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
Register for self-paced course

0A035G – Advanced Predictive Modeling Using IBM SPSS Modeler

This course builds on the courses Classifying Customers Using IBM SPSS Modeler (V16) and Predicting Continuous Targets Using IBM SPSS Modeler (V16). It presents advanced techniques to predict categorical and continuous targets. Before reviewing the modeling techniques, data preparation issues are addressed such as partitioning and detecting anomalies. Also, a method to reduce the number of fields to a number of core fields, referred as components or factors, is presented. The next two modules focus on advanced predictive models, such as Decision List, Support Vector Machines and Bayes Net. Following this presentation, two modules present methods to combine individual models into a single model in order to improve predictive power, including running and evaluating many models in a single run, both for categorical and continuous targets.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
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0A037G – Advanced Predictive Modeling Using IBM SPSS Modeler

This course builds on the courses Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18) and Predictive Modeling for Continuous Targets Using IBM SPSS Modeler (v18). It presents advanced techniques to predict categorical and continuous targets. Before reviewing the modeling techniques, data preparation issues are addressed such as partitioning and detecting anomalies. Also, a method to reduce the number of fields to a number of core fields, referred to as components or factors, is presented. Advanced classification models, such as Decision List, Support Vector Machines and Bayes Net, are reviewed. Methods are presented to combine individual models into a single model in order to improve predictive power, including running and evaluating many models in a single run, both for categorical and continuous targets.

  • In-class
  • Instructor Led Online

0A045G – Clustering and Association Modeling Using IBM SPSS Modeler

Clustering and Association Modeling Using IBM SPSS Modeler (v16) is a one day, instructor-led course that is designed to introduce participants to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Students will explore various clustering techniques that are often employed in market segmentation studies. Students will also explore how to create association models to find rules describing the relationships among a set of items, and how to create sequence models to find rules describing the relationships over time among a set of items.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
Register for self-paced course

0A047G – Clustering and Association Modeling Using IBM SPSS Modeler

Clustering and Association Modeling Using IBM SPSS Modeler (v18) introduces modelers to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Students will explore various clustering techniques that are often employed in market segmentation studies. Students will also explore how to create association models to find rules describing the relationships among a set of items, and how to create sequence models to find rules describing the relationships over time among a set of items.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
Register for self-paced course

0A055G – Advanced Data Preparation Using IBM SPSS Modeler

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

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
Register for self-paced course

0A057G – Advanced Data Preparation Using IBM SPSS Modeler

Advanced Data Preparation Using IBM SPSS Modeler (v18) covers advanced topics to aid in the preparation of data for a successful data mining project. Students will learn how to use functions, deal with missing values, use advanced field operations, handle sequence data, apply advanced sampling methods, and improve efficiency.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
Register for self-paced course

0A0G2G – Automated Data Mining with IBM SPSS Modeler

This class covers the use of IBM SPSS Modeler to automate the building of predictive models. Learn how to build predictive models for customer behavior and build customer segmentation using various cluster models. Students will learn how to read data from various sources and automatically prepare data for modeling using a variety of methods. Scoring new data using the model will also be discussed.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
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0A0U5G – Classifying Customers Using IBM SPSS Modeler

Classifying Customers Using IBM SPSS Modeler (V16) is an intermediate level course that provides an overview of how to use IBM SPSS Modeler to predict the category to which a customer belongs. Students will be exposed to rule induction models such as CHAID and C and R Tree. They will also be introduced to traditional statistical models and machine learning models. Although this course focuses on classifying customers (including students, patients, employees, and so forth), the techniques can also be applied to business questions such as predicting breakdown of machine parts.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
Register for self-paced course