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Registration for the Fall 2019 semester begins June 25.  Watch your email for more details.

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Special Topics in Data Analytics

Course Code: CSIS 4260
Faculty: Commerce & Business Administration
Credits: 3.0
Semester: 15
Learning Format: Lecture, Lab, Seminar
Typically Offered: TBD. Contact Department Chair for more info.
course overview

Students will learn about emerging technologies and trends in Data Analytics. This course is divided into several modules. Each module represents a specialized body of knowledge focusing on the technical aspects of the 4V's of big data ( Volume, Velocity, Variety, and Veracity) as well as policy and other aspects such as privacy and ethics. Students will also get a chance to research state-of-the-art Data Analytics in an industry of their choice. This course will provide students the required breadth to jumpstart their career in the Data Analytics field.

Course Content

  • Module 1 (2 weeks): Capturing, managing and using data for decision making.
  • Module 2 (3 weeks): Using tools for mining different types of data such as structured data, text data, and web data. 
  • Module 3 (3 weeks): Building the technology stack for Data Analytics in terms of 4 V’s of big data, i.e. Volume, Velocity, Variety, and Veracity.
  • Module 4 (3 weeks): Research Data Analytics in different industries. 
  • Module 5 (1 week): Workshop on data security issues.
  • Module 6 (1 week): Workshop on ethics and privacy issues.

Methods of Instruction

Lecture, seminar and hands-on exercises in the lab

Means of Assessment

Class Participation 0% - 5%
Assignments (min 2) 20% - 30%
Quizzes (min 2) * 10% - 15%
Group Project * 20% - 25%
Final Examination * 25% - 35%
Total 100%

# Some of the assessments may involve group work.

*In order to pass the course, students must, in addition to receiving an overall course grade of 50%, also achieve a grade of at least 50% on the combined weighted examination components (including quizzes, tests, exams).

Learning Outcomes

  1. Describe the technical requirements of managing extensive amounts of data.
  2. Use various tools to manipulate, map and reduce a large data set.
  3. Design and implement a technical solution to deal with 4 V’s (Volume, Velocity, Variety, and Veracity) of big data.
  4. Appraise data scaling strategies such as different types of partitioning and replication in relation to different data growth and data consumption scenarios.
  5. Evaluate the state of Data Analytics in a chosen industry.
  6. Explain concepts of data security with regards to Analytics data in storage and in trasmission.
  7. Discuss the basic principles of ethical conduct in relation to Data Analytics.
  8. Discuss the basic principles of data privacy in relation to Data Analytics.

course prerequisites

CSIS 3300 OR CSIS 3360

Corequisites

Courses listed here must be completed either prior to or simultaneously with this course:

  • No corequisite courses

Equivalencies

Courses listed here are equivalent to this course and cannot be taken for further credit:

  • No equivalency courses

curriculum guidelines

Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester/year of the course, consider the previous version as the applicable version.

course schedule and availability
course transferability

Below shows how this course and its credits transfer within the BC transfer system. 

A course is considered university-transferable (UT) if it transfers to at least one of the five research universities in British Columbia: University of British Columbia; University of British Columbia-Okanagan; Simon Fraser University; University of Victoria; and the University of Northern British Columbia.

For more information on transfer visit the BC Transfer Guide and BCCAT websites.

assessments

If your course prerequisites indicate that you need an assessment, please see our Assessment page for more information.

noticecurriculum notice

There is an upcoming curriculum change scheduled for .
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