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Unit information: Modelling Analytics in 2021/22

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Modelling Analytics
Unit code EFIMM0142
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Kremantzis
Open unit status Not open




School/department School of Management
Faculty Faculty of Social Sciences and Law

Description including Unit Aims

Model-based tools to inform organisational decisions have been around for decades but crucially have developed in line with advances in digital computing. This entails a requirement to not only understand the analytical principles of the various techniques and methodologies and where they may usefully be deployed, (sometimes in concert) but also the ability to formulate and execute such models on digital platforms using appropriate data. This domain of expertise is now commonly known as prescriptive analytics.

1. to offer a range of analytical model-based tools which have been shown to aid decision-making in practice;

2. to know sufficient of the range of available tools to be able to make an informed choice of which is appropriate for a given managerial problem;

3. to be exposed to specialist software which, along with appropriate data, can be deployed to formulate and execute various models;

4. to be able to convey the results from modelling a managerial situation to the relevant stakeholders and to respond to their concerns;

5. to be capable of synthesising approaches should that offer a better means to address the managerial problem.

Intended Learning Outcomes

On completion of this unit, students will be able to:

ILO1. demonstrate an understanding of the various concepts, techniques and methodologies which comprise the range of model-based tools for managerial decision-making;

ILO2. analyse a business problem and identify and apply appropriate prescriptive analytic techniques to inform an appropriate solution;

ILO3. understand the differences between deterministic and stochastic models, the data required for each and the sorts of problem each is suited for;

ILO4. demonstrate a sound knowledge of mathematical optimisation techniques (along with verification and validation methods);

ILO5. utilise a variety of digital platforms which support computer-based prescriptive analytics.

ILO6. appreciate the need to synthesise tools and methodologies in any evaluation of managerial requirements as part of the decision process.

Teaching Information

Teaching will be conducted through ten lectorial sessions of 3 hours. These will comprise lecture talks, computer sessions and problem-solving in small groups.

Assessment Information

Formative assessment (ILOs 1, 3 and 5)

During the lab sessions, small-group assessments will be undertaken which will involve the formulation of a model-based solution to a given managerial problem. This activity will require access to computer software which enables a solution to be reached. Formative feedback will be given and will aid the students for when they have to complete their individual summative project assessment.

Summative assessment

Group assessment (40% of the overall unit mark): Students will work in small groups to resolve a business problem. Each group will construct an optimisation model that represents the essence of the problem (ILOs 1-4), determine the appropriate values of the model parameters, and develop a computer-based procedure for deriving solutions to the problem from this model (ILOs 5-6). Each group will submit a 1500-word project report to communicate their findings.

Individual assessment (60% of the overall unit mark): Students will be given a 5-day window to complete their answers to assessment questions. This will evaluate the students’ understanding of core concepts in modelling analytics by testing the ability to formulate the business problems using appropriate optimisation techniques and computational skills to identify the optimal solution without using any optimisation solver (ILOs 1-4).


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How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the Faculty workload statement relating to this unit for more information.

The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates within the Regulations and Code of Practice for Taught Programmes.