Data Visualization

Curriculum Guideline

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Course
CSIS 3860
Discontinued
No
Course Code
CSIS 3860
Descriptive
Data Visualization
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

Seminar: 2 hours

Total: 4 hours

Method(s) Of Instruction
Lecture
Seminar
Learning Activities

Delivery will be by lecture, case study and assignments.  

Course Description
In this course, students will learn the skills to present analytics results in a clear, concise and visually appealing manner. This hands-on course will introduce students to various tools and techniques of data visualization, visualization best practices, and common pitfalls. Use of Data Visualization tools such as Tableau is adopted in this course for the hands-on skills. Students will also work on building targeted dashboards based on their audience’s need. Other tools such as d3.js, dc.js, Google Charts, etc. are also introduced to reflect on the variety of data visualization tools available for a data analyst to visualize the results of analysis.
Course Content
  1. Introduction to Big Data Analytics
  2. The importance of analytics and visualization in today's data-prevalent markets
  3. Introduction to Data Visualization using tools such as Tableau
  4. Effective ways of visualizing data using other data visualization tools such as free and/or Open Source tools – Ex: D3.JS, DC.JS, Google Charts, etc.
  5. Diverse types of Visual analysis – Time-Series, Deviation, Distribution and Correlation Analysis
  6. Interface components of a visualization tool such as Tableau
  7. The right visualization tool for different data sets – making the right choice
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. Explain core skills for Information Visualization and available visualization tools available in market
  3. Demonstrate the use of data visualization tools such as Tableau
  4. Explore other data visualization tools such as D3.JS or Google Charts, etc.
  5. Examine effective ways of visual analysis
  6. Create compelling and effective interactive dashboards.
  7. Incorporate geospatial visualization in Dashboards
  8. Publish Dashboards
  9. Choose the right visualization tool for different data sets
Means of Assessment

Assignments (min 3)                                          10% - 20%

Term Project – 1                                                 05% - 10%

Quizzes (min 2)                                                 10% - 15%

Midterm Examination                                          25% - 30%

Final Examination *                                            30% - 40%

Total                                                                         100%

* 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, and 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

Textbooks and Materials to be Purchased by Students:

 Recommended References:        

  • Now You See It: Simple Visualization Techniques for Quantitative Analysis  By Stephen Few ISBN-10: 0970601980 and ISBN-13: 978-0970601988
  • An Introduction to Data Visualization in JavaScript - Visual Story Telling With D3 By Ritchie S. King ISBN:13: 978032193317-1 and ISBN:10: 0-321-93317-6
Prerequisites

Principles of Math 12 with a C or Pre-Calculus 12 with a C or equivalent 

OR currently active in:

PDD Data Analytics

PBD Computer and Information Systems

Equivalencies

 

 
Which Prerequisite

NIL