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Predictive Modeling with PASW Modeler - Independent Study

Course description:

This course is delivered over three weeks and 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. You will also learn how to combine two or more models to improve prediction. Syllabus is provided for each week's study and materials are completed privately by each participant.

US Customers - Select this link to begin course registration
Canadian Customers - Select this link to begin course registration

Please note: If you are paying by Check or PO, you will need to download the Public Training or the Academic Training registration form and fax or mail the completed form to SPSS along with your payment.

Subscribers: This course is not available as part of the Training Subscription Service.

Duration and Cost:

Costs are for United States courses only. Information about other countries can be found at the worldwide offices page.

Who Should Attend:

This course follows either "Introduction to PASW Modeler and Data Mining" or “Advanced Data Preparation with PASW Modeler” is essential for anyone who wishes to become familiar with the full range of modeling techniques available in PASW Modeler to create predictive models.

Prerequisite:

General computer literacy. Experience using PASW Modeler (formerly Clementine), including familiarity with the PASW Modeler environment, creating streams, reading in data files, assessing data quality and handling missing data (including the type and data audit nodes), basic data manipulation (including the derive and select nodes), and creation of models. Prior completion of “Introduction to PASW Modeler and Data Mining” is required and completion of “Advanced Data Preparation with PASW Modeler” is strongly encouraged. An introductory course in statistics, or equivalent experience, would be helpful for the statistics-based modeling techniques.

Course Content:

The full table of contents for this course is available.