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MODULE DESCRIPTOR
Module Title
Data Mining
Reference CMM704 Version 2
Created October 2017 SCQF Level SCQF 11
Approved May 2016 SCQF Points 15
Amended November 2017 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 Discuss, compare and contrast the advantages and disadvantages of applying a specific data mining technique to a given learning task.
2 Use a toolkit to develop a data mining application tailored to a given learning task.
3 Effectively interpret the results of learning through an understanding of the strengths and limitations of data mining technology and the selection of an appropriate evaluation technique.
4 Demonstrate knowledge of the state-of-the-art in data mining and an awareness of current areas of research.
5 Apply and, where necessary, adapt an appropriate data mining technique to 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.

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: Examination Weighting: 70% Outcomes Assessed: 1, 3, 4, 5
Description: Closed book examination.
Component 2
Type: Practical Exam Weighting: 30% Outcomes Assessed: 2
Description: Practical examination.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The calculation of the overall grade for this module is based on 70% weighting of C1 and 30% weighting of C2 components.
Examination:
Practical Exam: A B C D E F NS
A A B B C D E
B A B C C D E
C B B C D D E
D B C C D E E
E B C D D E F
F E E E E E F
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