Course
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
202030
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
- 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
Learning outcomes
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.
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).
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.