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COMP40370

Academic Year 2025/2026

Data Mining (COMP40370)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Professor Tahar Kechadi
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

The course is structured in such a way as to present important concepts of data mining and how these concepts are implemented and used in real-world applications. The key idea behind this course is to integrate the theory and practice of data mining with many references to real-world problems and cases to illustrate the concepts and the implementation issues as we go through the lectures. The first chapter is devoted to a brief introduction to some background information needed to understand the material. This is followed by data warehouse topic and how different is from database concept. The notion of data mining process is explained and how it relates to the complete KDD process, as it is very important to understand that data mining is not an isolated subject. We will then overview a survey of some techniques used to implement data mining algorithms. We will follow by studying some core topics of data mining; classification ,clustering, and association rules. Other concepts, such as prediction, regression , and pattern matching, will also be covered, but viewed as special cases of the three core topics. In each concept we will only concentrate on the most popular techniques and algorithms.

About this Module

Learning Outcomes:

- Why Data Mining and what is Data Mining?
- What is Data Warehouse and its architecture?
- Understand multi-dimensional data model.
- Understand the data pre-processing phase.
- Understand core functions of Data Mining.
- Classification, clustering and association rules .

Indicative Module Content:

~

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Practical

24
Autonomous Student Learning

76
Total

124

Approaches to Teaching and Learning:
The delivery of this module will consist of 2 lectures per week for 12 weeks.
The planned 12-week tutorial session is designed to delve into key algorithms and concepts presented in the lectures. Each session will be 2 hours and will include:
1 Weekly Structure: Duration: 2 hours per week, Total: 12 weeks
2. Focus Areas: In-depth exploration of popular algorithms. Detailed discussions on critical concepts
3. Hands-on Data Mining: Utilize a data mining software tool. Analyze various datasets.
4. Learn about: The data mining process. Advantages and disadvantages of data mining.
This interactive approach will enhance understanding and provide practical experience in data analysis.

While we have two practical/tutorial sessions scheduled per week to reduce clashing with other modules, each student must choose only one session.

Note that if students are permitted to use generative AI tools in assignments, that will be indicated in the assignment specification.

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
Exam (In-person): This final exam, which usually takes place by the end of the semester, is organised by the UCD exams office. End of trimester
Duration:
2 hr(s)
 Graded No
50
 No
Practical Skills Assessment: The module has a weekly practical session. The students will be asked to submit some of the practical works. There will be two practical submissions during the semester. Week 7, Week 12 Graded No
25
 No
Quizzes/Short Exercises: These are short assessments organized every five weeks. They consist of in-class exercises, quizzes, and MCQ-type questions. Week 6, Week 11 Graded No
25
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

The students will be given feedback on their tutorial or practical work within 2 weeks following their submissions.

Name Role
Cheng Xu Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Thurs 11:00 - 11:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Tues 13:00 - 13:50
Autumn Practical Offering 1 Week(s) - Autumn: Weeks 2-12 Tues 16:00 - 17:50
Autumn Practical Offering 2 Week(s) - Autumn: Weeks 2-12 Wed 13:00 - 14:50
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