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.

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- In-class
- Instructor Led Online
- Self-Paced Virtual Classroom

#### 0A0V5G – Predicting Continuous Targets Using IBM SPSS Modeler

Predicting Continuous Targets Using IBM SPSS Modeler (v16) is an intermediate level course that provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values. Students will be exposed to rule induction models such as CHAID and C&R Tree. They will also be introduced to traditional statistical models such as Linear Regression. Machine learning models will also be presented. Business use case examples include: predicting the length of subscription (for newspapers, telecommunication, job length, and so forth) and predicting claim amount (insurance).

- In-class
- Instructor Led Online
- Self-Paced Virtual Classroom

#### 0A0V7G – Predictive Modeling for Continuous Targets Using IBM SPSS Modeler

This course (formerly Predicting Continuous Targets Using IBM SPSS Modeler (v16)) provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values. Students will be exposed to rule induction models such as CHAID and C&R Tree. They will also be introduced to traditional statistical models such as Linear Regression. Machine learning models will also be presented. Business use case examples include: predicting the length of subscription (for newspapers, telecommunication, job length, and so forth) and predicting claim amount (insurance).

- In-class
- Instructor Led Online
- Self-Paced Virtual Classroom

#### 0A105G – Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler

Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (V16) teaches users how to analyze text data using IBM SPSS Modeler Text Analytics. Students will see the complete set of steps involved in working with text data, from reading the text data to creating the final categories for additional analysis. After the final model has been created, there is an example of how to apply the model to perform Churn analysis. Topics include how to automatically and manually create and modify categories, how to edit synonym, type, and exclude dictionaries, and how to perform Text Link Analysis and Cluster Analysis with text data. Also included are examples of how to create resource templates and Text Analysis packages to share work with other projects and other users.

- In-class
- Instructor Led Online
- Self-Paced Virtual Classroom

#### 0A107G – Introduction to IBM SPSS Text Analytics

This course (formerly: Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (v16)) teaches students how to analyze text data using IBM SPSS Modeler Text Analytics. You will be introduced to the complete set of steps involved in working with text data, from reading the text data to creating the final categories for additional analysis. After the final model has been created, there is an example of how to apply the model to perform churn analysis in telecommunications. Topics include how to automatically and manually create and modify categories, how to edit synonym, type, and exclude dictionaries, and how to perform Text Link Analysis and Cluster Analysis with text data. Also included are examples of how to create resource templates and Text Analysis packages to share work with other projects and other users.

- In-class
- Instructor Led Online
- Self-Paced Virtual Classroom

#### 0E032G – Predictive Modeling with IBM SPSS Modeler SPVC

Predictive Modeling with IBM SPSS Modeler demonstrates how to develop models to predict categorical and continuous outcomes, using such techniques as neural networks, decision trees, logistic regression, support vector machines, and Bayesian network models. Use of the binary classifier and numeric predictor nodes to automate model selection is included. Feature selection and detection of outliers are discussed. Expert options for each modeling node are reviewed in detail and advice is provided on when and how to use each model. Students will also learn how to combine two or more models to improve prediction.

**Please note: ***This course is only available in the self-paced format. *The in-class version of this training has been retired by IBM, and has been replaced by 0A0U5 – Classifying Customers Using IBM SPSS Modeler.

- Self-Paced Virtual Classroom

#### 0G046G – Introduction to IBM SPSS Neural Networks

This course will cover the usage and application of Neural Network models using IBM SPSS Statistics. Neural Network models are used to predict an outcome variable that is either categorical or interval in scale using predictors that are also categorical or interval. Neural networks are popular when modeling complex environments (for example financial applications).

- In-class
- Instructor Led Online
- Self-Paced Virtual Classroom

#### 0G056G – Correspondence Analysis and Multidimensional Scaling with IBM SPSS Categories

This course will focus on how to perform Correspondence Analysis and Multi-Dimensional Scaling using procedures in the IBM SPSS Categories add-on module in IBM SPSS Statistics. Learn how to use correspondence analysis to examine the relationship of categorical data and display these relationships on perceptual maps. Learn about multidimensional scaling and preference scaling techniques to examine similarities and dissimilarities among objects such as product brands and features and customer preferences. These techniques are useful in any circumstance where you need to analyze and display graphically the correspondence among categorical data. The course will discuss the basic logic of these techniques, how to setup the analysis and examine the results using a variety of usage examples and hands-on exercises.

- In-class
- Instructor Led Online
- Self-Paced Virtual Classroom

#### 0G063G – Advanced Techniques ANOVA

This two-day course focuses on the different Analysis of Variance techniques which allow you to test whether the means of several populations are the same. After discussing the basic assumptions for each technique you will check the assumptions, run the analysis and draw conclusions from the data.

- In-class
- Instructor Led Online
- Self-Paced Virtual Classroom

#### 0G073G – Advanced Techniques: Regression

Advanced Techniques: Regression is a 3 day course that examines regression techniques used to explore the relationships between interval scale variables in detail. Students will develop an understanding of when to apply each technique, how to apply it and how to interpret the results. Additionally, the course will cover some preliminary data analysis steps, how to check the underlying assumptions and suggestions of how to proceed when your assumptions fail.

- In-class
- Instructor Led Online
- Self-Paced Virtual Classroom