|Course code||0E104||Skill level||Intermediate|
|Duration||2.0 days||Delivery type||Self-paced Virtual Class|
|Course type||Public only|
|Public price||See Partner website|
THIS IS A SELF-PACED VIRTUAL CLASS (or Web Based Training). AFTER YOU START THE COURSE, YOU HAVE 30 DAYS TO COMPLETE IT.
Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form.
After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.
You can start the course at any time within 12 months of enrolling for the course. After you start the course, you have 30 days to complete your course. Within this 30 days, 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.
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.
Introduction to IBM SPSS Modeler Text Analytics is a two-day instructor led classroom course that teaches users how to analyze text data using IBM SPSS Modeler Text Analytics. Students will see the complete set of steps involved in working with text data, from reading the text data to creating the final categories for additional analysis. After the final model has been created, there is an example of how to apply the 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. Also included are examples of how to create resource templates and Text Analysis packages to share work with other projects and other users.
Training Paths that reference this course are:
You should have:
Practical experience with coding text data is not a prerequisite but would be helpful.
Introduction to Text Mining
A Text Mining Example
Reading Text Data
Linguistic Analysis and Text Mining
Creating a Text Mining Concept Model
Extracted Results in the Interactive Workbench
Editing Advanced Resources
Text Link Analysis
Managing Linguistic Resources
Using Text Mining Models