Continuing Education

CCTM352 - IBM Predictive Analytics Modeler

Course Code

A unique identifier used at NAIT for this specific course.

Campus

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Course Overview

Data scientists are tasked with breaking down big data into usable information so to help derive business value. This course content is comprised of introductory and advanced topics. The introductory portion reviews concepts of data science and the stages of a data science project. The advanced portion covers advanced topics to aid in the preparation of data for a successful data science project.

Topics Covered

Introductory Section:
• Introduction to data science
• Introduction to data science using IBM SPSS Modeler
• Collecting initial data
• Understanding the data
• Setting the unit of analysis
• Integrating data
• Deriving and reclassifying fields
• Identifying relationships
• Introduction to modeling

Advanced Section:
• How to use functions
• Deal with missing values
• Use advanced field operations
• Handle sequence data
• Apply advanced sampling methods
• Improve efficiency

Please note that this course is offered by NAIT in partnership with IBM. Accordingly, this outline originates from (was created by) IBM. If you wish to read IBM’s course outline, please follow this link: https://educationservices.bowriversolutions.com/predictive-analytics-modeler/

View Course Outline

Upcoming Offerings

This course is part of these programs or course groups:

IBM Digital Micro-credentials


Additional Resources

Delivery Methods

  • Face to Face: Where: In-person meetings. When: Course is scheduled at a specific time for students to attend. Face-to-face instruction at all class meetings. Location may be on campus or at a worksite.
  • Blended: Where: Mixture of in-person & online components. When: Course is scheduled at a specific time for students to attend. Combination of face-to-face and online components at specific times. Some online components may be accessed online anytime.
  • Hyflex: Where: Choice to attend in-person or online meetings. When: Course is scheduled at a specific time for students to attend. For each class, students choose to attend in-person with the instructor or online at a specific time.
  • Remote Live Delivery: Where: Online with instructor. When: Course is scheduled at a specific time for students to attend. Instruction is delivered at set times online. Students do not come to campus.
  • Remote On-Demand Delivery: Where: Online anytime. When: No set class meetings. Coursework is accessed on-demand and online. While there are no set class meetings, there may be set due dates and deadlines for some activities. Students may interact with peers through virtual tools.
  • Remote Independent: Where: Online anytime. When: No set class meetings. Coursework is accessed on-demand and online, with no instructor support. While students choose when to do coursework, there may be set due dates and deadlines. 
  • Work Placement: Where: In-person meetings. When: Work is scheduled at a specific time for students to attend. Onsite work integrated learning. Location at a worksite.
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