MIS41270 Data Management and Mining

Academic Year 2022/2023

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

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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.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Multiple components Varies over the Trimester n/a Graded No

100


Carry forward of passed components
Yes
 
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
Spring
     
Lecture Offering 1 Week(s) - 20, 21, 23, 24, 25, 26, 29, 30, 32, 33 Mon 09:00 - 10:50
Lecture Offering 2 Week(s) - 20, 21, 23, 24, 25, 26, 29, 30, 32, 33 Mon 11:30 - 13:20
Spring
     

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