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IBM Advanced Analytics Courses

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0A007G – Introduction to IBM SPSS Modeler and Data Mining

This course provides the fundamentals of using IBM SPSS Modeler and introduces the participant to data mining. 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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0A008G – Introduction to IBM SPSS Modeler and Data Science v18.1.1

This course provides the fundamentals of using IBM SPSS Modeler and introduces the participant to data science. The principles and practice of data science 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

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
  • Self-Paced Virtual Classroom
Register for self-paced course

0A038G – Advanced Predictive Modeling Using IBM SPSS Modeler

This course presents advanced models to predict categorical and continuous targets. Before reviewing the models, data preparation issues are addressed such as partitioning, detecting anomalies, and balancing data. The participant is first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core fields, referred to as components or factors. The next units focus on supervised models, including Decision List, Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed to combine supervised models and execute them in a single run, both for categorical and continuous targets.

  • 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

0A048G – Clustering and Association Modeling Using IBM SPSS Modeler

Clustering and Association Modeling Using IBM SPSS Modeler (v18.1.1) introduces modelers to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Participants will explore various clustering techniques that are often employed in market segmentation studies. Participants 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

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

0A058G – Advanced Data Preparation Using IBM SPSS Modeler

This course covers advanced topics to aid in the preparation of data for a successful data science 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

0A0U7G – Predictive Modeling for Categorical Targets Using IBM SPSS Modeler

Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18) (formerly Classifying Customers Using IBM SPSS Modeler) focuses on analytical models to predict a categorical field (churn, fraud, response to a mailing, pass/fail exams, machine break-down, and so forth). Students will be introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. The student will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

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

0A0U8G – Predictive Modeling for Categorical Targets Using IBM SPSS Modeler

This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

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