Fundamentals of Data Analytics

Curriculum Guideline

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Course
CSIS 3360
Discontinued
No
Course Code
CSIS 3360
Descriptive
Fundamentals of Data Analytics
Department
Computing Studies & Information Systems
Faculty
Commerce & Business Administration
Credits
3.00
Start Date
End Term
Not Specified
PLAR
No
Semester Length
15 Weeks
Max Class Size
35
Contact Hours
Lecture: 2 hours per week Seminar: 2 hours per week Total: 4 hours per week
Method(s) Of Instruction
Lecture
Seminar
Learning Activities

Lecture, seminar and hands-on exercises in the lab

Course Description
In this course, students will gain the basic understanding of the emerging Data Analytics field. The students will be required to work with real-world examples using current computing tools. Integral to the course is a group project where students will complete a variety of tasks including: requirement elicitation; developing hypothesis; data exploration; dimensional analysis; identifying metrics; and visual presentation of results.
Course Content
  1. Introduction to Big Data Analytics
  2. Data Analytics Lifecycle
  3. Data Mining Process
  4. Review Basic Data Analytics Methods and planning data analytic steps
  5. Business Intelligence Trends and Big Data Trends
  6. Make use of MS Excel pivot tables for analytics
  7. Exploring the use of one of the data analytics tools – Tableau among many out there
  8. Advanced Analytics – Technology and Tools
  9. Database Analytics using Tableau
  10. Decision Analysis through designing visualizations
Learning Outcomes

At the end of this course, the successful student will be able to:

  1. Explain foundations of Big Data Analytics & Data Mining Process
  2. Describe modern approach to Business Intelligence / Data Analytics
  3. Analyse Business Intelligence Trends & Trends in Big Data
  4. Utilize effective ways to analyze data
  5. Develop data analytics plan
  6. Use data analytic tools such as Tableau
  7. Explore Advanced Analytics – Technology and Tools.
  8. Explain philosophies, tools and techniques of decision analysis in terms of data management and data visualization.
Means of Assessment
   
Assignments/Project:    10% - 25%
Quizzes (Minimum 2)  10% - 20%
Midterm exam  20% - 30%
Final Exam * 30% - 40%
Total 100%

Some of the assessments may involve group work.

* Practical hands-on computer exam

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.

Textbook Materials

No Text Required, Notes to be provided by Instructor

References:         

EMC Education Services.  Data Science & Big Data Analytics - Latest Ed., Wiley

Tableau documentation / guides.

Prerequisites

min grade of C in CSIS 2200 AND (BUSN 2429 or MATH 1160)

(Note: CSIS 2300 is recommended)

Students are expected to be comfortable using MS Excel. For those needing upgrading, CSIS 1190 is recommended.

Equivalencies

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

  • No equivalency courses