Lecture, seminar and hands-on exercises in the lab
- Module 1 (2 weeks): Capturing, managing and using data for decision making.
- Module 2 (3 weeks): Using tools for mining different types of data such as structured data, text data, and web data.
- Module 3 (3 weeks): Building the technology stack for Data Analytics in terms of 4 V’s of big data, i.e. Volume, Velocity, Variety, and Veracity.
- Module 4 (3 weeks): Research Data Analytics in different industries.
- Module 5 (1 week): Workshop on data security issues.
- Module 6 (1 week): Workshop on ethics and privacy issues.
- Describe the technical requirements of managing extensive amounts of data.
- Use various tools to manipulate, map and reduce a large data set.
- Design and implement a technical solution to deal with 4 V’s (Volume, Velocity, Variety, and Veracity) of big data.
- Appraise data scaling strategies such as different types of partitioning and replication in relation to different data growth and data consumption scenarios.
- Evaluate the state of Data Analytics in a chosen industry.
- Explain concepts of data security with regards to Analytics data in storage and in trasmission.
- Discuss the basic principles of ethical conduct in relation to Data Analytics.
- Discuss the basic principles of data privacy in relation to Data Analytics.
|Assignments (min 2)||20% - 30%|
|Quizzes (min 2) *||10% - 15%|
|Group Project *||20% - 25%|
|Final Examination *||25% - 35%|
# Some of the assessments may involve group work.
*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).
Instructor compiled materials
other textbooks as approved by the department
Courses listed here must be completed either prior to or simultaneously with this course:
- No corequisite courses
Courses listed here are equivalent to this course and cannot be taken for further credit:
- No equivalency courses