Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Data Mining Techniques | |||
| Reference | CMM010 | Version | 1 |
| Created | May 2022 | SCQF Level | SCQF 11 |
| Approved | June 2022 | SCQF Points | 15 |
| Amended | ECTS Points | 7.5 | |
| Aims of Module | |||
|---|---|---|---|
| To provide students with the skills to undertake Data Mining projects using current Data Mining tools and techniques. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Compare and contrast the use of different data mining techniques for given learning tasks. |
| 2 | Apply and adapt appropriate data mining techniques to a given problem. |
| 3 | Carry out a data mining project following a data mining methodology while considering the legal, ethical and security implications of the project. |
| 4 | Critically evaluate and interpret the results of data mining through the selection of appropriate evaluation techniques. |
| Indicative Module Content |
|---|
| Data mining concepts. Data mining methodology (e.g., CRISP-DM). Data mining tasks, techniques, and algorithms. Evaluation and bias. Legal, ethical and security issues. Case studies. |
| 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 | 30 | 30 |
| Non-Contact Hours | 120 | 120 |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | 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: | Practical Exam | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | This practical exam will consist of a practical application and evaluation of data mining techniques to a given problem. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| To achieve a pass in this module requires a minimum of grade D in Assessment Component 1 | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | A in Component 1 |
| B | B in Component 1 |
| C | C in Component 1 |
| D | D in Component 1 |
| E | E in Component 1 |
| F | F in Component 1 |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| 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 Analytics 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 | OLSON, DL. (2019), Descriptive Data Mining, 2nd Edition. Springer Nature. |
| 6 | JAMES, G. WITTEN, D. HASTIE, T. TIBSHIRANI, R. (2021) An Introduction to Statistical Learning : with Applications in R. Springer |