Curriculum Guideline

Special Topics in Data Analytics

Effective Date:
Course
Course Code
CSIS 4260
Descriptive
Special Topics in Data Analytics
Department
Computing Studies & Information Systems
Faculty
Commerce & Business Administration
Credits
3.00
Start Date
End Term
201930
PLAR
No
Semester Length
15
Max Class Size
35
Contact Hours
Lecture: 2 hours per week Lab/Seminar: 2 hours per week Total: 4 hours per week
Method Of Instruction
Lecture
Lab
Seminar
Methods Of Instruction

Lecture, seminar and hands-on exercises in the lab

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 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.
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).

Textbook Materials

Instructor compiled materials

or

other textbooks as approved by the department

Prerequisites
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