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MIS41270

Academic Year 2024/2025

Data Management and Mining (MIS41270)

Subject:
Management Information Systems
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
8
Module Coordinator:
Professor Michael O'Neill
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

The module provides an introduction to Data Mining and Management in the context of business organisations.

About this Module

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 Hours:
Student Effort Type Hours
Lectures

24

Specified Learning Activities

48

Autonomous Student Learning

120

Total

192


Approaches to Teaching and Learning:
Lectures and Practical Work

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered

Not yet recorded.


Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 

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