Course

Special Topics in Data Analytics

Faculty
Commerce & Business Administration
Department
Computing Studies & Information Systems
Course code
CSIS 4260
Credits
3.00
Semester length
15
Max class size
35
Method(s) of instruction
Lecture
Lab
Seminar
Typically offered
To be determined

Overview

Course description
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.
Learning activities

Lecture, seminar and hands-on exercises in the lab

Means of assessment
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).

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.

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.
Textbook materials

Instructor compiled materials

or

other textbooks as approved by the department

Requisites

Prerequisites

min grade C in (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

Course 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.

(Current)

Course Transfers

These are for current course guidelines only. For a full list of archived courses please see https://www.bctransferguide.ca

Institution Transfer details for CSIS 4260
Athabasca University (AU) AU COMP 3XX (3)
College of New Caledonia (CNC) CNC CSC 2XX (3)
Kwantlen Polytechnic University (KPU) No credit
Simon Fraser University (SFU) No credit
Thompson Rivers University (TRU) TRU COMP 4XXX (3)
University Canada West (UCW) UCW CMPT 4XX (3)
University of Northern BC (UNBC) No credit
University of the Fraser Valley (UFV) UFV CIS 4XX (3)

Course Offerings

Winter 2025

CRN Days Instructor Status
Maximum seats
35
Currently enrolled
35
Remaining seats:
0
On waitlist
3
Building
New Westminster - North Bldg.
Room
N6109
Times:
Start Time
11:30
-
End Time
14:20
Section notes

CSIS 4260 001 - This section is restricted to PBD Data Analytics Stream and PDD Data Analytics students.

CRN Days Instructor Status
Maximum seats
35
Currently enrolled
35
Remaining seats:
0
On waitlist
2
Building
New Westminster - North Bldg.
Room
N5111
Times:
Start Time
9:30
-
End Time
12:20
Section notes

CSIS 4260 002 - This section is restricted to PBD Data Analytics Stream and PDD Data Analytics students.