Module Database Search



MODULE DESCRIPTOR
Module Title
Data Mining
Reference CM2712 Version 2
Created January 2023 SCQF Level SCQF 8
Approved June 2019 SCQF Points 30
Amended June 2023 ECTS Points 15

Aims of Module
To provide students with an understanding of the main principles underlying Data Mining techniques and the ability to apply current Data Mining tools to datasets.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Discuss the advantages and disadvantages of applying a specific data mining technique to a given learning task.
2 Apply and adapt appropriate data mining techniques to a given problem.
3 Evaluate and interpret the results of data mining through the selection of an appropriate evaluation technique.
4 Demonstrate knowledge of current strengths, limitations and ethical use of data mining technology.

Indicative Module Content
Data mining concepts. Data mining methodology and life cycle (e.g., CRISP-DM). Data mining types (e.g., supervised and unsupervised). Data mining tasks (e.g., classification, clustering, regression). Data mining algorithms (e.g., Decision tree, random forest, SVM, KNN). Data mining applications. Ethical issues and potential bias in data mining.

Module Delivery
The module is delivered in Blended Learning mode using structured online learning materials/activities and directed study, facilitated by regular online tutor support. Workplace Mentor support and work-based learning activities will allow students to contextualise this learning to their own workplace. Face-to-face engagement occurs through annual induction sessions, employer work-site visits, and modular on-campus workshops.

Indicative Student Workload Full Time Part Time
Contact Hours 30 N/A
Non-Contact Hours 30 N/A
Placement/Work-Based Learning Experience [Notional] Hours 240 N/A
TOTAL 300 N/A
Actual Placement hours for professional, statutory or regulatory body 240  

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
Description: This coursework will consist of a practical data mining development exercise, and a discussion on potential applications of data mining within the workplace.

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

Module Requirements
Prerequisites for Module None, in addition to course entry requirements.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 WITTEN, I.H. et al., 2017. Data mining: practical machine learning tools and techniques. 4th ed. Amsterdam, Netherlands: Morgan Kaufmann.
2 NETTLETON, D., 2014. Commercial data mining: processing, analysis and modeling for predictive analitycs projects. Amsterdam, Netherlands: Morgan Kaufmann.
3 MOHAMMED, J.Z. and WAGNER, M., 2014. Data mining and analysis: fundamental concepts and algorithms. Cambridge: Cambridge University Press.
4 ZAO, Y., 2013. R and data mining. Examples and case studies. Amsterdam, Netherlands: Academic Press.
5 PORCU, V., 2018. Python for data mining quick syntax reference. New York, NY: Apress.

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