- Introduction to Big Data Analytics
- Data Analytics Lifecycle
- Data Mining Process
- Review Basic Data Analytics Methods and planning data analytic steps
- Business Intelligence Trends and Big Data Trends
- Make use of MS Excel pivot tables for analytics
- Exploring the use of one of the data analytics tools – Tableau among many out there
- Advanced Analytics – Technology and Tools
- Database Analytics using Tableau
- Decision Analysis through designing visualizations
Lecture, seminar and hands-on exercises in the lab
|Assignments/Project:||10% - 25%|
|Quizzes (Minimum 2)||10% - 20%|
|Midterm exam||20% - 30%|
|Final Exam *||30% - 40%|
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.
At the end of this course, the successful student will be able to:
- Explain foundations of Big Data Analytics & Data Mining Process
- Describe modern approach to Business Intelligence / Data Analytics
- Analyse Business Intelligence Trends & Trends in Big Data
- Utilize effective ways to analyze data
- Develop data analytics plan
- Use data analytic tools such as Tableau
- Explore Advanced Analytics – Technology and Tools.
- Explain philosophies, tools and techniques of decision analysis in terms of data management and data visualization.
No Text Required, Notes to be provided by Instructor
EMC Education Services. Data Science & Big Data Analytics - Latest Ed., Wiley
Tableau documentation / guides.
(Note: CSIS 2300 is recommended)
Students are expected to be comfortable using MS Excel. For those needing upgrading, CSIS 1190 is recommended.
No corequisite courses.
Courses listed here are equivalent to this course and cannot be taken for further credit:
- No equivalency 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.
|Institution||Transfer Details||Effective Dates|
|Athabasca University (AU)||AU COMP 3XX (3)||2017/01/01 to -|
|Coquitlam College (COQU)||No credit||2015/01/01 to -|
|Kwantlen Polytechnic University (KPU)||No credit||2015/01/01 to -|
|Langara College (LANG)||LANG STAT 2XXX (3)||2015/01/01 to -|
|Northern Lights College (NLC)||No credit||2018/01/01 to -|
|Okanagan College (OC)||OC COSC 2XX (3)||2016/09/01 to -|
|Simon Fraser University (SFU)||SFU STAT 2XX (3)||2015/01/01 to -|
|University Canada West (UCW)||UCW CPSC 2XX (3)||2015/01/01 to -|
|University of Northern BC (UNBC)||UNBC CPSC 2XX (3)||2015/01/01 to -|
|University of the Fraser Valley (UFV)||UFV COMP 3XX (3)||2015/01/01 to -|
|University of Victoria (UVIC)||UVIC CSC 2XX (1.5)||2015/01/01 to -|