|Kurskod||0E003SE||Leveranstyp||Self-paced Virtual Class|
SEK 9 375,00 exkl moms
SEK 11 718,75 inkl moms
PLEASE DO NOT MAKE TRAVEL ARRANGEMENT FOR THIS SELF PACED VIRTUAL COURSE (SPVC). 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.
NO EXTENSIONS FOR THIS COURSE WILL BE GRANTED.
This is the self paced training version of ""Introduction to IBM SPSS Modeler and Data Mining (V14.2)"" classroom course. Introduction to IBM SPSS Modeler and Data Mining (V14.2) is a two day self paced training course that provides an overview of data mining and the fundamentals of using IBM SPSS Modeler., The principles and practice of data mining are illustrated using the CRISP-DM methodology. 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. The course provides training in the basics of how to read, explore, and manipulate data with IBM SPSS Modeler, and then create and use successful models.
You should have:
It would be helpful if you had an understanding of your organization's data, as well as any of your organization's business issues that are relevant to the use of data mining.
Introduction to Data Mining
Working with Streams
Data Mining Tour
Collecting Initial Data
Setting the Unit of Analysis
Deriving and Filling Fields
Reclassifying and Binning Fields
Looking for Relationships
Introduction to Classification