Overview:
- Credits:
- 7.5
- Level:
- 4
- Semester:
- Semester Two
- Subject:
- Management Information Systems
- School:
- Business
- Coordinator:
- Professor Michael O'Neill
MIS40970 Data Mining for Business Analytics
Academic Year 2018/2019
Core.The explosion of data, big (in terms of volume, velocity, variety and veracity) and small, creates new demands and opportunities for organisations, and requires a combination of business knowledge, computing power and algorithms alongside human intuition and even creativity to explore, analyse and extract value from this data. In this module we explore the data mining process in order to extract value from business data to empower informed decision making.
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Curricular information is subject to change
Learning Outcomes:
On completion of the module students should be able to:
● Outline the Data Mining process
● Compare and Contrast the different data mining activities
● Understand the strengths and weaknesses of data mining as part of a decision support environment
● Apply data mining algorithms
| Student Effort Type | Hours |
|---|---|
| Lectures | 36 |
| Specified Learning Activities | 48 |
| Autonomous Student Learning | 100 |
| Total | 184 |
Requirements, Exclusions and Recommendations
Not applicable to this module.
| Description | % of Final Grade | Timing |
|---|---|---|
| Examination: < Description > | 60 |
2 hour End of Trimester Exam |
| Continuous Assessment: < Description > | 40 |
Varies over the Trimester |
Compensation
This module is not passable by compensation
Resit Opportunities
End of Semester Exam
Remediation
If you fail this module you may repeat, resit or substitute where permissible.
| Name | Role |
|---|---|
| Dr Gilyana Borlikova | Lecturer / Co-Lecturer |
| MSc Stefan Forstenlechner | Tutor |
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