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Course Outlines

Course outlines are provided here for your information and reference as they become available and have received the required approvals.

Available BCIT course outlines can be accessed by selecting the term, the course subject and the course number from the dropdown list(s) below. If outlines are available for your selected term and course, they will then be listed. If any of these steps don’t result in what you are looking for, it indicates that no outline is currently available. If you are looking for an outline from a current term please check back closer to the start of the course or consult another outline from a previous term.  Please keep in mind that the content from previous outlines may be different than current offerings of the course. For historical outlines not covered in this system please contact BCIT Library at 604-432-8370 to see if they have the outline you require.

The course outline is a statement of educational intent and direction, providing BCIT students with clear, concise, accurate and readily available information related to course content and administration. BCIT course outlines are governed by Policy 5403 and the creation of course outlines is subject to the procedure described in 5403-PR1.

Subject
Computer Systems - COMP

Course
7611

While no outlines currently exist for this course, below are the course learning outcomes/competencies.

Course Learning Outcomes/Competencies

Upon successful completion of this course, the student will be able to:

  • Discuss business intelligence / data mining applications and related issues.
  • Prepare and preprocess data for data mining using techniques such as aggregation, sampling, dimensionality reduction, subset selection, discretization, binarization and variable transformation.
  • Identify the use of appropriate measures of similarity and dissimilarity between data objects.
  • Describe knowledge discovery, business intelligence and the various data mining concepts and algorithms, such as classification, association, clustering and anomaly detection.
  • Apply appropriate model/non-model base and algorithm using a data mining tool.
  • Explain key architecture components and issues of large data stores.
  • Design and implement a data mining group project from end-to-end.
  • Conduct an individual research paper on a data mining algorithm and/or tool for a business application.

Effective as of Fall 2018

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