Overview:
- Credits:
- 7.5
- Level:
- 4
- Semester:
- Spring
- Subject:
- Management Information Systems
- School:
- Business
- Coordinator:
- Professor Michael O'Neill
MIS41270 Data Management and Mining
Academic Year 2023/2024
The module provides an introduction to Data Mining and Management in the context of business organisations.Show/hide contentOpenClose All
Curricular information is subject to change
Learning Outcomes:
On completion of the module students should be able to:
● Compare and contrast different Data Management approaches
● Understand the scope of a Data Strategy including Data Governance requirements
● 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 to different types of data including text data
● Understand the similarities and differences between Machine Learning and Data Mining
| Student Effort Type | Hours |
|---|---|
| Lectures | 24 |
| Specified Learning Activities | 48 |
| Autonomous Student Learning | 120 |
| Total | 192 |
Requirements, Exclusions and Recommendations
Not applicable to this module.
Module Requisites and Incompatibles
Not applicable to this module.
| Resit In | Terminal Exam |
|---|---|
| Summer | No |
Feedback Strategy/Strategies
• Feedback individually to students, post-assessment
How will my Feedback be Delivered?
Not yet recorded.
| Name | Role |
|---|---|
| Elayne Ruane | Lecturer / Co-Lecturer |
| Joseph Carnec | Tutor |
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