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.
- Introduction to Big Data Analytics
- The importance of analytics and visualization in today's data-prevalent markets
- Introduction to Data Visualization using tools such as Tableau
- 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.
- Diverse types of Visual analysis – Time-Series, Deviation, Distribution and Correlation Analysis
- Interface components of a visualization tool such as Tableau
- The right visualization tool for different data sets – making the right choice
Methods of Instruction
Delivery will be by lecture, case study and assignments.
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%
* 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).
At the end of this course, the successful student will be able to:
- Explain foundations of Big Data Analytics & Data Mining Process
- Explain core skills for Information Visualization and available visualization tools available in market
- Demonstrate the use of data visualization tools such as Tableau
- Explore other data visualization tools such as D3.JS or Google Charts, etc.
- Examine effective ways of visual analysis
- Create compelling and effective interactive dashboards.
- Incorporate geospatial visualization in Dashboards
- Publish Dashboards
- Choose the right visualization tool for different data sets
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
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.
Below shows how this course and its credits transfer within the BC transfer system.
A course is considered university-transferable (UT) if it transfers to at least one of the five research universities in British Columbia: University of British Columbia; University of British Columbia-Okanagan; Simon Fraser University; University of Victoria; and the University of Northern British Columbia.
For more information on transfer visit the BC Transfer Guide and BCCAT websites.
If your course prerequisites indicate that you need an assessment, please see our Assessment page for more information.