Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Optimisation For Decision Support | |||
| Reference | CM4128 | Version | 1 |
| Created | September 2021 | SCQF Level | SCQF 10 |
| Approved | August 2022 | SCQF Points | 15 |
| Amended | ECTS Points | 7.5 | |
| Aims of Module | |||
|---|---|---|---|
| To provide students with necessary practical skills and underlying knowledge to critically appraise and develop decision-support systems. To enable students to compare and contrast suitable optimization techniques for decision-support and select the most appropriate method for a given business context. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Use a suitable framework to model business data and apply statistical analysis techniques. |
| 2 | Identify, describe and compare different types of decision-support systems, their properties and appropriate business contexts for their use. |
| 3 | Theoretically discuss and practically apply optimization techniques to develop solutions for small to large-scale problems. |
| 4 | Critically evaluate and visualize the output of optimization techniques and analyse this output in relation to relevant outcomes within a given business context. |
| Indicative Module Content |
|---|
| Basic data modelling: identification and selection of features from business data, evaluation and selection of methodologies. Data analysis techniques: Monte Carlo analysis, statistical techniques, forecasting, nearest neighbour retrieval. Context of decision-support systems: typical use-cases and data, evaluating decision-support systems and their outcomes. Developing decision-support systems: types of decision-support system, development strategies, alternatives. Optimization techniques: local optimization, global optimization, gradient-based techniques, evolutionary algorithms. Commercial modelling platforms, including GAMS. |
| Module Delivery |
|---|
| This module uses the following delivery modes: • Guided study (lectures, tutorials and other learning materials delivered through VLE + bibliography) • Practical lab exercises • Personal study Key concepts are introduced and illustrated through lectures (physical and virtual).The understanding of students is tested and further enhanced through interactive lab tutorials. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 36 | N/A |
| Non-Contact Hours | 114 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | N/A |
| 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 |
| Description: | Individual portfolio assessment comprised of a theoretical use-case analysis and practical programming exercise | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The module assessment pattern consists on one component so the grade in the component is the grade for the module as a whole. | |
| 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 | BURSTEIN, F., HOLSAPPLE, C. W., 2008. Handbook on Decision Support Systems. Springer |
| 2 | EMC EDUCATION SERVICES, 2015. Data Science and Big Data Analytics: Discovering, Analysing, Visualizing and Presenting Data |
| 3 | KOCHENDERFER, M. J., and WHEELER, T. A., 2019. Algorithms for optimization. MIT Press. |
| 4 | MOHAMMED, J.Z. and WAGNER, M., 2014. Data mining and analysis: fundamental concepts and algorithms. Cambridge: Cambridge University Press. |
| 5 | MORENO-JIMÉNEZ, J. M. et al., eds. 2020. Decision Support Systems X: Cognitive Decision Support Systems and Technologies: 6th International Conference on Decision Support System Technology. |
| 6 | SIEGEL, E. 2016. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. John Wiley & Sons. |