Postgraduate Course: Performance Analytics with DEA: Basic Concepts and Methods (CMSE11424)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course provide students with fundamental theory of Data Envelopment Analysis (DEA). |
| Course description |
Academic Description:
Data Envelopment Analysis (DEA) is a performance evaluation and bench-marking methodology. DEA is a non-parametric and frontier-based methodology that benchmarks against the best or the worst practice frontier. Nowadays, DEA is commonly used for the relative performance evaluation and risk assessment of entities such as banks, bank branches, firms listed on stock markets, investment vehicles including projects, production technologies, suppliers, etc. This course aims at training students in the field of performance evaluation and management using DEA as the main methodology. The course shall cover the key concepts in performance management along with a general classification of performance evaluation methodologies; DEA concepts and generic methodology; static black-box DEA models and their use in business applications; and practical issues in DEA and how to address them.
Outline Content:
1. Key concepts in performance management along with a general classification of performance evaluation methodologies
2. DEA concepts and generic methodology
3. Static DEA models and their use in business applications
4. Practical issues in DEA and how to address them
Student Learning Experience:
Weekly lectures and hands-on programming exercises in Matlab and DEA solvers which enables students to implement the methodologies covered in class.
Tutorial/seminar hours represent the minimum total live hours - online or in-person - a student can expect to receive on this course. These hours may be delivered in tutorial/seminar, lecture, workshop or other interactive whole class or small group format. These live hours may be supplemented by pre-recorded lecture material for students to engage with asynchronously.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary. |
Course Delivery Information
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| Academic year 2022/23, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
88 )
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| Additional Information (Learning and Teaching) |
Seminar/Tutorial hrs are the min total live hrs, online or in-person, students can expect to receive
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% coursework (individual) - assesses all course Learning Outcomes |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Discuss the concept and methods of performance measurement, evaluation, and management using the proper terminology
- Identify and properly state performance problems in different business settings
- Address performance problems within a static DEA framework and choose the right basic DEA models to devise solutions
- Formulate managerial guidelines in the area of performance management and make recommendations based on basic DEA analyses
- Communicate performance problems and basic solutions effectively and efficiently to a critical audience
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Reading List
Cooper WW, Seiford LM and Tone K. (2007) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Second Edition. Springer
Resource List:
https://eu01.alma.exlibrisgroup.com/leganto/public/44UOE_INST/lists/26181441810002466?auth=SAML |
Additional Information
| Graduate Attributes and Skills |
Numeracy and Big data
Knowledge integration and application
Analytical thinking
Written communication
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| Keywords | Not entered |
Contacts
| Course organiser | Prof Jamal Ouenniche
Tel: (0131 6)50 3792
Email: |
Course secretary | Ms Emily Davis
Tel: (0131 6)51 7112
Email: |
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