- 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
Delivery will be by lecture, case study and assignments.
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).
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.
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
Textbooks and Materials to be Purchased by Students:
- Learning Tableau by Joshua Milligan
- Tableau Desktop Manual/Documentation/Help http://www.tableausoftware.com/support/help
- Now You See It: Simple Visualization Techniques for Quantitative Analysis By Stephen Few ISBN-10: 0970601980 and ISBN-13: 978-0970601988
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
No corequisite courses.
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.
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 3860|
|Athabasca University (AU)||AU COMP 3XX (3)|
|Capilano University (CAPU)||CAPU COMP 1XX (3)|
|College of the Rockies (COTR)||COTR COMP 3XX (3)|
|Coquitlam College (COQU)||No credit|
|Kwantlen Polytechnic University (KPU)||KPU SOBU 1XXX (3)|
|Northern Lights College (NLC)||NLC CPSC 3XX (3)|
|Okanagan College (OC)||No credit|
|Thompson Rivers University (TRU)||TRU COMP 3XXX (3)|
|University of British Columbia - Okanagan (UBCO)||UBCO DATA 1st (3)|
|University of British Columbia - Vancouver (UBCV)||UBCV CPSC 1st (3)|
|University of Northern BC (UNBC)||UNBC CPSC 1XX (3)|
|University of the Fraser Valley (UFV)||UFV COMP 1XX (3)|
|University of Victoria (UVIC)||UVIC CSC 1XX (1.5)|
This section is restricted to PBD Data Analytics Stream and PDD Data Analytics students.