本资料提供方：

一、IBM SPSS Statistics

    IBM SPSS Statistics是世界领先的统计软件。它使您能够更快速、更深入地挖掘数据，这使其成为比电子表格、数据库或标准多维工具更高效的分析工具。SPSS Statistics擅长理解复杂的模式和关联----使最终用户能够得出结论并进行预测。

    SPSS Statistics的主要功能包括线性模型，非线性模型，模拟建表，自定义表，数据准备，缺失值和数据有效性，决策树，预测等。更多内容见：

http://public.dhe.ibm.com/common/ssi/ecm/zh/yts03009cnzh/YTS03009CNZH.PDF

    下载IBM SPSS Statistics Desktop Trial version 21.0.0

http://www14.software.ibm.com/download/data/web/en_US/trialprograms/W110742E06714B29.html

    教程

    见第三部分，IBM学院计划。

二、IBM SPSS Modeler简介

IBM SPSS Modeler 的前身是Clementine，是一个数据挖掘工作台，用于帮助您快速直观地构建预测模型，而无需进行编程。其精密的数据挖掘技术使您能够对结果进行建模，了解哪些因素会对结果产生影响。

更多内容见：

http://public.dhe.ibm.com/common/ssi/ecm/zh/ytd03124cnzh/YTD03124CNZH.PDF

三、IBM学院计划（Academic Initiative）SPSS相关课件

1. IBM学院计划简介

IBM 学院计划是一个向全球大学院校的老师及研究单位的研究人员，提供可免费下载/使用的教学或研究资源的平台。

学院计划首页

https://www.ibm.com/developerworks/university/academicinitiative/

高校老师可免费申请成为IBM学院计划成员，具体申请步骤见下面的链接：

http://www-03.ibm.com/ibm/university/academic/pub/page/mem_join

参赛同学可联系所在院校的指导老师，由老师申请成为学院计划成员后，下载/使用SPSS相关资源。

2. IBM学院计划 SPSS相关课件

2.1 IBM SPSS Statistics相关课件（需要用学院计划帐号登陆后下载）

(SP0G507) Introduction to IBM SPSS Statistics

Get up to speed in IBM SPSS Statistics (formerly SPSS Statistics) quickly and easily in this two-day course. The course guides you through the fundamentals of using IBM SPSS Statistics for typical data analysis process. Learn the basics of reading data, data definition, data modification, and data analysis and presentation of your results. See how easy it is to get your data into IBM SPSS Statistics so that you can focus on analyzing the information. In addition to the fundamentals, learn shortcuts that will help you save time.

https://www14.software.ibm.com/webapp/iwm/web/reg/download.do?source=ai-course-bao&S_PKG=KAOSJ&lang=en

(SP0G517) Introduction to Statistical Analysis Using IBM SPSS Statistics

This course introduces students to the statistical component of SPSS. This is an application-oriented course and the approach is practical. Students look at several statistical techniques and discuss situations in which you would use each technique, the assumptions made by each method, how to set up the analysis using SPSS 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 gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, and interpret their output, and graphically display the results using SPSS. This course uses the IBM SPSS Statistics Base features.

https://www14.software.ibm.com/webapp/iwm/web/reg/download.do?source=ai-course-bao&S_PKG=7P5CL&lang=en

(SP0G527) Data Management and Manipulation with IBM SPSS Statistics

This course focuses on the use of a wide range of transformation techniques, ways to automate work, manipulate data files and results, and send your output to other Windows applications. Students gain an understanding of the various options for operating SPSS and how to use syntax to perform data transformations efficiently. This course uses the SPSS for Windows Base features as well as the Data Preparation Add-on Module in Appendix A.

When taught by IBM Training instructors, this course spans two days.

https://www14.software.ibm.com/webapp/iwm/web/reg/download.do?source=ai-course-bao&S_PKG=RWPOG&lang=en

2.2 IBM SPSS Modeler相关课件

(SP0A002) Introduction to IBM SPSS Modeler and Data Mining

This course provides an overview of data mining and the fundamentals of using IBM SPSS Modeler. Using the CRISP-DM methodology, the course illustrates the principles and practice of data mining. The course structure follows the stages of a typical data mining project, from reading data, to data exploration, data transformation, modeling, and effective interpretation of results. Students learn the basics of how to read, explore, and manipulate data with IBM SPSS Modeler, and then create and use successful models.

https://www14.software.ibm.com/webapp/iwm/web/reg/download.do?source=ai-course-bao&S_PKG=MV3R7&lang=en

(SP0A052) Advanced Data Preparation with IBM SPSS Modeler

In this course, students examine additional topics to aid in the preparation of data for a successful data mining project. Students learn how to partition records from files, handle missing data, modify fields and create new fields, and work with dates, strings and sequence data.

https://www14.software.ibm.com/webapp/iwm/web/reg/download.do?source=ai-course-bao&S_PKG=E7HST&lang=en

(SP0A032) Predictive Modeling with IBM SPSS Modeler

This course 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. This course also covers feature selection and detection of outliers. Expert options for each modeling node are reviewed in detail and advice is provided on when and how to use each model. Students also learn how to combine two or more models to improve prediction.

https://www14.software.ibm.com/webapp/iwm/web/reg/download.do?source=ai-course-bao&S_PKG=NBFNT&lang=en

(SP0A042) Clustering and Association Models with IBM SPSS Modeler

This course demonstrates how to segment or cluster data with all the clustering techniques available in IBM SPSS Modeler. The course also provides examples of creating association models to find rules describing the relationships among a set of items, and of creating sequence models to find rules describing the relationships over time among a set of items.

https://www14.software.ibm.com/webapp/iwm/web/reg/download.do?source=ai-course-bao&S_PKG=HLNGI&lang=en

(SP0A102) Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler

This course shows students how to analyze text data using IBM SPSS Text Analytics. The complete set of steps involved in working with text data, from reading text data to creating the final categories for additional analysis is covered. There is an example of how to apply the final 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. Examples of how to create resource templates and Text Analysis packages to share your work with other projects and other users are included.

https://www14.software.ibm.com/webapp/iwm/web/reg/download.do?source=ai-course-bao&S_PKG=KKY75&lang=en