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IBM Advanced Analytics Courses

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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
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0A0V8G – Predictive Modeling for Continuous Targets Using IBM SPSS Modeler

This course 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. Students are introduced to machine learning models, such as Neural Networks. Business use case examples include: predicting the length of subscription for newspapers, telecommunication, and job length, as well as predicting insurance claim amounts.

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

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
Register for self-paced course

0A108G – Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler

This course (formerly: Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (v18)) teaches you how to analyze text data using IBM SPSS Modeler Text Analytics. Students 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 with other projects and other users.

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

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. 

  • Self-Paced Virtual Classroom
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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
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0G505G – Introduction to IBM SPSS Statistics (v24)

Introduction to IBM SPSS Statistics (v24) guides students through the fundamentals of using IBM SPSS Statistics for typical data analysis process. Students will learn the basics of reading data, data definition, data modification, and data analysis and presentation of analytical results. Students will also see how easy it is to get data into IBM SPSS Statistics so that they can focus on analyzing the information. In addition to the fundamentals, students will learn shortcuts that will help them save time. This course uses the IBM SPSS Statistics Base features.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
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0G515G – Introduction to Statistical Analysis Using IBM SPSS Statistics (v24)

Introduction to Statistical Analysis Using IBM SPSS Statistics (v24) provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. Students will review several statistical techniques and discuss situations in which they would use each technique, the assumptions made by each method, how to set up the analysis, as well as how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing underlying relationships. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output, and graphically display the results. This course uses the IBM SPSS Statistics Base features.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
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0G525G – Data Management and Manipulation with IBM SPSS Statistics (V24)

Data Management and Manipulation with IBM SPSS Statistics is a two day course on the use of a wide range of transformation techniques, ways to automate data preparation work, manipulate data files and analytical results. Students will gain an understanding of the various options for controlling the IBM SPSS Statistics operating environment and how to perform data transformations efficiently.This course uses the IBM SPSS Statistics Base features.

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom
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0K2K9G – Introduction to IBM SPSS Decision Trees (v19) SPVC

Introduction to IBM SPSS Decision Trees is a two day self-paced training course that covers the principles and practice of the tree-based decision and regression methods available in IBM SPSS Decision Trees. A general introduction to the features of the IBM SPSS Decision Trees module and an overview of decision tree based methods will be covered. These methods (CHAID, Exhaustive CHAID, CRT, and QUEST) are used to perform classification, segmentation, and prediction modeling in a wide range of business and research areas. The techniques are discussed and compared, analyses are performed, and the results interpreted.

  • Self-Paced Virtual Classroom
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