| Unit name | Modelling Analytics |
|---|---|
| Unit code | EFIMM0142 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Kremantzis |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Management - Business School |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important?
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 and gives us the opportunity to:
How does this unit fit into your programme of study
This unit embraces a comprehensive and sought-after skill set for students, who would be interested in pursuing a PhD degree or a specialist career in management science/modelling analysis related fields. Businesses, organisations and governments are now turning to prescriptive analytics to solve complex problems. Students will be offered the opportunity to deepen into widely used and explored mathematical programming, optimisation, and modelling techniques to solve real-world problems; this will, in turn, let them make more well-argued decisions and convince the key stakeholders of their significance.
An overview of content
Students will be given the chance to thoroughly explore widely used topics in management science and operations research such as linear optimisation, integer optimisation, transportation and assignment problems, network algorithms, and multi-objective decision-making (goal programming).
How will students, personally, be different as a result of the unit
On completion of this unit, students will realise that knowledge of modelling analytics and particularly optimisation, plays a key role in various sectors involving marketing, finance, operations, and supply chain management. They will be able to select appropriate (mathematical programming) techniques for different situations and then implement and interpret the result of the analysis into useful information/insights for key stakeholders.
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,
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 will be conducted through ten lectorial sessions of 3 hours. These will comprise a combination of pre-recorded lecture talks, on-campus problem-solving workshops focusing on the practical aspects of the main theoretical lecture, on-campus small group computer lab sessions, and optional online advice and feedback hour sessions for addressing more questions (if any). Additional online quizzes and case studies will be provided on Blackboard to support self-directed learning
Tasks which help you learn and prepare you for summative tasks (formative):
Tasks which count towards your unit mark (summative):
Group Assignment (50% of the overall unit mark):
Students will work in groups to resolve a real-life 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 Assignment (50% of the overall unit mark):
This will consist of a supervised, physical sit-in exam, lasting up to 2 hours and worth 50% of the total unit grade.
This summative assessment is designed to evaluate students’ understanding of core concepts discussed in the class throughout the semester. Specifically, it will assess their ability to:
It directly aligns with ILOs 1–5 of the unit, focusing on the practical and theoretical understanding of optimisation methods, quantitative modelling, and decision-making frameworks.
When assessment does not go to plan
When a student fails the unit and is eligible to resubmit, failed components will be reassessed on a like-for-like basis.
The initial component involves a 50% individual reflective piece, limited to 1,000 words. This reflection task requires students to critically analyse both the opportunities and challenges they encountered while collaborating on the original group assignment. To ensure alignment with ILOs 1-6, students must explicitly discuss how their experiences contributed to their understanding of optimization modelling, parameter selection, and computational problem-solving (ILOs 1-4), as well as their ability to make key decisions and develop problem-solving strategies (ILOs 5-6). Additionally, students should reflect on group dynamics, teamwork, and personal learning, evaluating what worked well, what could be improved, and how these insights will shape their future academic or professional approaches.
The second component entails a supervised, physical sit-in exam, lasting up to 2 hours and worth 50% of the total unit grade. This re-assessment serves as an alternative to the original physical sit-in exam and will comprehensively address all the methodologies extensively covered throughout the semester. It aligns with ILOs 1–5, focusing on the practical and theoretical understanding of optimisation methods, quantitative modelling, and decision-making frameworks.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. EFIMM0142).
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 University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
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. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
