|Course code||0E002||Skill level||Basic|
|Duration||3.0 days||Delivery type||Self-paced Virtual Class|
|Course type||Public only|
|Public price||See Partner website|
ONCE YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT.
After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. You will then have a 30 day window in which to complete your course. Within this 30 days, you will have 336 hours of elab time. The self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day, but lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system. Once you have accessed the course, help is available Monday through Friday; questions will be responded to within 24 hours.
If the course requires a remote lab system, the lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system.
NO EXTENSIONS FOR THIS COURSE WILL BE GRANTED.
This is the self paced training version of 'Introduction to IBM SPSS Modeler and Data Mining' classroom course. This course will show you how to use IBM SPSS Modeler to automate the building of predictive models. The course will show you how to build predictive models for customer behavior and build customer segmentation using various cluster models. You will learn how to read data from various sources and automatically prepare data for modeling using a variety of methods. Scoring new data using the model will also be discussed.
This basic course is for anyone with little or no experience using IBM SPSS Modeler (formerly Clementine) or with data mining in general.
You should have:
It would be helpful if you had:
No statistical background is necessary.