IB9SS-15 Prescriptive Analytics and Optimisation
Introductory description
The main objective of the module is to provide sound theoretical backgrounds through a variety of real life applications of optimisation models in analytics and data analysis. The module also includes an assignment that involves individually generated tasks to be solved by using computational technologies.
Module aims
The module aims to develop the student's interest in, and knowledge and understanding of, various optimisation models to support decision making in organisations. Students will learn about the theoretical underpinnings of these models as well as how they are used in applications. They will gain practical experience in modelling and problem solving.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Optimisation modelling: mathematical programming, including extensive studies of linear programming and dynamic programming; integer programming, introduction to non-linear optimisation, introduction to algorithms and heuristics. The techniques mentioned are illustrated on a range of real life applications in business analytics and data science. IT tools (Excel, R or Python libraries, etc.) are used to demonstrate the usage of the theory in practice.
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate comprehensive knowledge of Operational Research techniques and effective problem solving and decision making skills.
- Critically evaluate and consider implications of analytical solutions in real-world settings
Indicative reading list
Reading lists can be found in Talis
Research element
Elements of research methodology typical for quantitative analysis (mathematical, analytical, computational approaches) are represented in examples discussed in the class
Interdisciplinary
The module includes a use of computer science and algorithms as a part of teaching material; that illustrate an interdisciplinary nature of approaches taught.
Subject specific skills
Formulate a model; Select the most efficient method to tackle the problem; Use appropriate software to solve the model; Report on the findings using a range of media which are widely used in business; Reflect on the validity of the model and the modelling process accepted.
Identify problem structures, to suggest solution approaches and to identify potential quality issues of the solution
Develop an adequate methodology and skills to extract, formalise and solve a structured model for analysis of messy real life decision support problems
Transferable skills
Numeracy skills
Written communication
Study time
| Type | Required |
|---|---|
| Online learning (scheduled sessions) | 5 sessions of 2 hours (7%) |
| Other activity | 18 hours (12%) |
| Private study | 48 hours (32%) |
| Assessment | 74 hours (49%) |
| Total | 150 hours |
Private study description
Private study to include preparation for lectures and own reading
Other activity description
9 x 2 hr F2F workshops
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group D
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| Group Quantitative Assignment: optimisation models to be set and solved (number of words N/A) | 30% | 22 hours | No |
Reassessment component |
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| Individual Assignment | Yes (extension) | ||
|
Individual Assignment based on group work |
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Assessment component |
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| 2 Hr Written Exam | 70% | 52 hours | No |
Reassessment component is the same |
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Feedback on assessment
For the assignment, correct solutions for all models will be provided. In case of wrong solutions, the typical mistakes will be commented on. For the exam, overall cohort feedback will analyse typical mistakes and will provide the model solutions.
Courses
This module is Optional for:
- Year 1 of TIBS-N1N3 Postgraduate Taught Business Analytics