Undergraduate Course: Decision Analytics (BUST10133)
Course Outline
| School | Business School |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course provides students with an understanding of the techniques available for the analysis of management problems in which uncertainty plays a significant role. |
| Course description |
The techniques used for the analysis of management problems are illustrated using examples based on applications from the areas of capacity planning, quality control, consumer behaviour, inventory management, finance and purchasing.
Syllabus
The course is comprised of four modules which cover four modelling techniques:
1. Markov Chains
2. Markov Decision Processes
3. Decision Analysis
4. Sequential Sampling
Student Learning Experience
1. Lectures explain the concepts underpinning four modelling techniques for management problems involving uncertainty and present a series of illustrative examples. Lectures are supported by suggested readings from the recommended texts. Students are advised to attend all lectures.
2. Students gain further experience in the application of the techniques to management problems by working through the example questions uploaded in the 'Tutorials' folder on Learn at their own pace, with feedback via online solutions.
3. Optional example class tutorials summarise each topic covered by reviewing a past examination question.
4. Additional web-based material provides students with feedback as they tackle further past examination questions.
5. The coursework project requires students to build a model of a management case study, to analyse the model using techniques covered in the course and to present the findings in a written report. Students will develop skills in the use of Microsoft Excel to support their analysis of the model.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Honours entry. |
Information for Visiting Students
| Pre-requisites | Visiting students should have at least 3 Business Studies courses at grade B or above (or be predicted to obtain this). We will only consider University/College level courses. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2018/19, Available to all students (SV1)
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Quota: 125 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 2,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
172 )
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| Additional Information (Learning and Teaching) |
6 hours directed learning; 172 hours independent learning
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| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
One 3000-word project report on Markov decision processes 30%; final degree exam 70%
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| Feedback |
1. Solutions to example questions and past exam questions posted on Learn throughout the course provides you with feedback on your understanding of modelling techniques covered and on your ability to apply these techniques.
2. Generic feedback on your COURSEWORK, together with individual marks, will be posted on Learn within 15 working days from the submission deadline; also the individual feedback for your coursework will be available to collect from the Business School UG Office (Room 1.11, Business School, 29 Buccleuch Place), but you will not be able to take away the original piece of coursework, as it may be required by the Board of Examiners.
3. The optional EXAMPLE CLASS TUTORIALS at the end of each module provide the opportunity to do further exercises and ask questions.
4. Your EXAMINATION marks will be posted on Learn (together with generic feedback and examination statistics) as soon as possible after the December Board of Examiners' meeting (normally end of January/beginning of February). You will be notified when you may come into the Business School UG Office (Room 1.11, Business School, 29 Buccleuch Place) to look at your examination scripts. Note that you will not be able to remove any examination scripts from the UG Office as they may be required by the Board of Examiners. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S1 (December) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Discuss critically the practical use of the techniques covered.
- Apply the modelling techniques covered to structure management problems.
- Solve models built using the techniques covered.
- Make inferences about a management problem based on the solution of a model built using the techniques covered.
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Reading List
Recommended:
1. Hamdy A Taha, Operations Research: an Introduction (9e) (Pearson, 2011).
Further suggestions posted on Learn. |
Additional Information
| Graduate Attributes and Skills |
Cognitive Skills
On completion of the course students will:
a) Demonstrate that they can identify which of the techniques covered is most suitable for a management problem.
b) Demonstrate that they can discuss the results of their analysis of a management problem in written reports.
Key Skills
On completion of the course students will:
a) Demonstrate that they can build and analyse a model of a real world management problem involving uncertainty.
b) Demonstrate their ability to apply their computer skills to support the analysis of a management problem involving uncertainty.
c) Demonstrate that they can present the findings of a quantitative analysis in a concise written report.
Subject Specific Skills
On completion of the course students will have developed their modelling skills. |
| Additional Class Delivery Information |
10 x 2-hour lectures; 1 x 2-hour computer lab; 4 x 1-hour example class tutorials. |
| Keywords | Decision Analytics |
Contacts
| Course organiser | Prof Tom Archibald
Tel: (0131 6)50 4604
Email: T.Archibald@ed.ac.uk |
Course secretary | Ms Patricia Ward-Scaltsas
Tel: (0131 6)50 3823
Email: Patricia.Ward-Scaltsas@ed.ac.uk |
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