- 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.
Lecture, seminar and hands-on exercises in the lab
|Assignments (min 2)||20% - 30%|
|Quizzes (min 2) *||10% - 15%|
|Group Project *||20% - 25%|
|Final Examination *||25% - 35%|
# 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).
Students may conduct research as part of their coursework in this class. Instructors for the course are responsible for ensuring that student research projects comply with College policies on ethical conduct for research involving humans, which can require obtaining Informed Consent from participants and getting the approval of the Douglas College Research Ethics Board prior to conducting the research.
- Describe the technical requirements of managing extensive amounts of data.
- Use various tools to manipulate, map and reduce a large data set.
- Design and implement a technical solution to deal with 4 V’s (Volume, Velocity, Variety, and Veracity) of big data.
- Appraise data scaling strategies such as different types of partitioning and replication in relation to different data growth and data consumption scenarios.
- Evaluate the state of Data Analytics in a chosen industry.
- Explain concepts of data security with regards to Analytics data in storage and in trasmission.
- Discuss the basic principles of ethical conduct in relation to Data Analytics.
- Discuss the basic principles of data privacy in relation to Data Analytics.
Instructor compiled materials
other textbooks as approved by the department
Courses listed here must be completed either prior to or simultaneously with this course:
- No corequisite courses
Courses listed here are equivalent to this course and cannot be taken for further credit:
- No equivalency courses
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.
|Institution||Transfer Details||Effective Dates|
|Athabasca University (AU)||AU COMP 3XX (3)||2017/01/01 to -|
|College of New Caledonia (CNC)||CNC CSC 2XX (3)||2017/01/01 to -|
|Simon Fraser University (SFU)||No credit||2017/01/01 to -|
|Thompson Rivers University (TRU)||TRU COMP 4XXX (3)||2017/01/01 to -|
|University Canada West (UCW)||UCW CMPT 4XX (3)||2017/01/01 to -|
|University of Northern BC (UNBC)||No credit||2020/01/01 to -|
|University of the Fraser Valley (UFV)||UFV CIS 4XX (3)||2017/01/01 to -|