NitroFlare IBM SPSS Modeler Techniques for Missing Data

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  1. kocogi

    kocogi Active Member

    May 29, 2012
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    IBM SPSS Modeler: Techniques for Missing Data
    MP4 | Video: 1280x720 | 19 kbps | 16 KHz | Duration: 2 Hours | 521 MB
    Genre: eLearning | Language: English

    IBM SPSS Modeler Seminar Series

    IBM SPSS Modeler is a data mining workbench that allows you to build predictive models quickly and intuitively without programming. Analysts typically use SPSS Modeler to analyze data by mining historical data and then deploying models to generate predictions for recent (or even real-time) data.

    Overview: Techniques for Missing Data is a series of self-paced videos (three hours of content). Students will learn how missing data is identified and handled in Modeler. Students also will learn different approaches to dealing with missing data including imputation of missing values, removing missing data, and running parallel streams with and without missing data. Students will also learn how to use the Type, Data Audit, and Filler nodes to identify and handle missing data.

    IBM SPSS Modeler: Techniques for Missing Data
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