IBM SPSS Modeler - Quick Start WBTIBM
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Häufig gestellte Fragen
No statistical or data mining background is necessary.
Was lernen Sie in diesem Kurs?
This course is developed and owned by an IBM Authorized Global Training Provider: Avnet Inc.
Derivative Works Courses are developed by an IBM authorized Global Training Provider. The content of the course is based on IBM products and services. It offers skills or services that IBM does not offer in the course catalog today and are intended to compliment our existing portfolio. The course is developed by the Training Provider. IBM solely provides a market place to advertise the courses on our external websites, ibm.com/training and lists the Global Training Provider who owns the content.
IBM SPSS Modeler - Quick Start is a seven hour self-paced, online interactive training course. You will have access to the web-based training for up to 30 days. IBM SPSS Modeler is a data mining workbench that helps you build predictive models quickly and intuitively, without programming. Analysts typically use SPSS Modeler to analyze data by doing data mining and then deploying models. Overview: IBM SPSS Modeler: QuickStart is a self-paced course. Normally, this course is a full day in length. However, you do not need to complete it in one day. You will learn the basics of data mining and using IBM SPSS Modeler. You will learn the fundamentals of the CRISP-DM process, navigating within Modeler, reading data and assessing data quality, combining files, constructing new fields, selecting cases, constructing/12/ision tree models, evaluating models, and deploying models.
If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.
- Discuss the Cross Industry Standard Process for Data Mining (CRISP-DM)
- Be familiar with the General Modeler Interface
- Read Data into Modeler
- Assess Data Quality
- Combine Data Files
- Derive Fields
- Select Cases
- Use/12/ision Trees to build Models
- Evaluate Models
- Deploy Models