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MODULE DESCRIPTOR
Module Title
Data Mining
Reference CMM704 Version 5
Created February 2024 SCQF Level SCQF 11
Approved May 2016 SCQF Points 15
Amended April 2024 ECTS Points 7.5

Aims of Module
To provide an understanding of the main principles underlying Data Mining applied to real-world datasets. To also provide specialised knowledge and valuable insights into algorithms that are at the forefront of machine learning research.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Appraise the advantages and disadvantages of applying a specific data mining technique to a given learning task.
2 Produce a data mining application tailored to a given learning task using an appropriate toolkit.
3 Evaluate the results of learning through an understanding of the strengths and limitations of data mining technology by selecting an appropriate evaluation technique.
4 Appraise the knowledge of the state-of-the-art in data mining and an awareness of current areas of research.
5 Deal with an appropriate data mining technique to solve a given problem.

Indicative Module Content
Basic data mining concepts. Implementation of fundamental learning approaches and attribute selection methods. Rules involving relations; incorporating domain knowledge in learning. State-of-the-art algorithms such as random forests, SVM and deep-learning principals including meta-learners. Advanced techniques for evaluating learned concepts. Comparative studies for Data Mining algorithms.

Module Delivery
This is a lecture-based course, supplemented with laboratory sessions, where a data mining toolkit is applied to varied learning tasks and tutorials where additional understanding is gained through practical exercises which supplement the lectures. For on-campus learners, teaching and learning will be facilitated hands-on at lecture halls and labs. For online learners teaching and learning will be facilitated in real-time via virtual classrooms using voice and video, collaborative tools, and remote assistance tools.

Indicative Student Workload Full Time Part Time
Contact Hours N/A 48
Non-Contact Hours N/A 102
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL N/A 150
Actual Placement hours for professional, statutory or regulatory body    

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type: Coursework Weighting: 100% Outcomes Assessed: 1, 2, 3, 4, 5
Description: Coursework submission and defence.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The calculation of the overall grade for this module is based on 100% weighing of C1. An overall minimum grade D is required to pass the module.
Module Grade Minimum Requirements to achieve Module Grade:
A The student needs to achieve an A in C1.
B The student needs to achieve a B in C1.
C The student needs to achieve a C in C1.
D The student needs to achieve a D in C1.
E The student needs to achieve an E in C1.
F The student needs to achieve an F in C1.
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module None except for course entry requirements.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 WITTEN, I., FRANK, E. and HALL M., 2011. Data Mining: Practical Machine Learning Tools and Techniques 3rd Ed. Morgan Kaufmann.
2 HAN,J., KAMBER, M. and PEI, J., 2011. Data Mining: Concepts and Techniques 3rd Ed, Morgan Kaufmann.
3 MITCHELL, T., 1997. Machine Learning. McGraw-Hill.
4 LANTZ, B., 2013. Machine Learning with R. Packt Publishing.
5 ZHAO, Y., 2012. R and Data Mining: Examples and Case Studies. Academic Press.

Robert Gordon University, Garthdee House, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781