Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Data Mining | |||
| Reference | CMM510 | Version | 9 |
| Created | February 2022 | SCQF Level | SCQF 11 |
| Approved | April 2005 | SCQF Points | 15 |
| Amended | July 2022 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To provide students with an understanding of the main principles underlying Data Mining and Machine Learning techniques and the ability to apply current Data Mining and Machine Learning tools to real datasets. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Critically discuss, compare and contrast the advantages and disadvantages of applying a specific data mining technique to a given learning task. |
| 2 | Use industry standard tools to develop a data mining application tailored to a given learning task and evaluate the results obtained. |
| 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. |
| Indicative Module Content |
|---|
| Data mining concepts. Implementation of fundamental learning approaches to classification, regression, clustering and association rules models. Bias-variance trade-off. Incorporating domain knowledge in learning. Advanced techniques for evaluating learned concepts. Calculation of confidence intervals for predictive performance. Comparison of data mining schemes. Boosting, bagging and stacking techniques. Applications. Legal, ethical, social and professional issues in data mining. |
| Module Delivery |
|---|
| This is a lecture based course, supplemented with laboratory sessions, where industry standard data mining software 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: | Practical examination applying data mining techniques to a given dataset. | ||||
| 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 except for course entry requirements. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | OLSON, DL. (2019), Descriptive Data Mining, 2nd Edition. Springer Nature. |
| 2 | WITTEN, IH. FRANK, E. HALL, MA, PAL, C. (2017) Data Mining - Practical Machine Learning Tools and Techniques, 4th ed. Morgan Kaufman. |
| 3 | NISBET, R. MINER, G. YALE, KP. (2018), Handbook of Statistical Analysis and Data Mining Applications, 2nd Edition, Academic Press. |
| 4 | AGGARWAL, CC. (2015). Data Mining: the Textbook. Springer. |
| 5 | CICHOSZ, P. (2015) Data Mining Algorithms: Explained Using R, Wiley. |
| 6 | JAMES, G. WITTEN, D. HASTIE, T. TIBSHIRANI, R. (2021) An introduction to statistical learning : with applications in R. Springer |