|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/ibm-spss-modeler-techniques-for-missing-data/
课程评论：没有评论
**课程名称：** IBM SPSS Modeler：缺失值处理技术 **课程概述：** 本课程是关于IBM SPSS Modeler的数据科学入门课程，让您无需编程即可快速直观地构建预测模型。SPSS Modeler作为一款数据挖掘工作台，可帮助分析师挖掘历史数据以发现模式，并部署模型来预测近期（甚至实时）数据。 **学习内容：** 本课程为一系列自定进度的视频课程，总时长约三小时。您将学习： * 如何在SPSS Modeler中识别和处理缺失数据。 * 处理缺失数据的不同方法，包括： * 缺失值插补。 * 删除包含缺失数据的数据。 * 运行并行数据流，分别处理包含和不包含缺失数据的情况。 * 如何使用Type、Data Audit和Filler节点来识别和处理缺失数据。 **目标受众：** 希望学习如何有效地在IBM SPSS Modeler中处理缺失数据的学生和数据分析师。
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.
