| 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 |
| Units you must take before you take this one (pre-requisite units) |
no |
| Units you must take alongside this one (co-requisite units) |
no |
| Units you may not take alongside this one |
n/a |
| 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 (network) problems, multi-objective decision making (goal programming), and deterministic and stochastic inventory models.
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
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. The amount of time necessary to spend in the self-directed study will be dependent on how deeply you wish to understand the concepts covered, but 120 – 170 hours over the TB2, may be taken as a ballpark figure.
Tasks which help you learn and prepare you for summative tasks (formative):
There will be weekly practical exercises and case studies (in workshops and labs) to be completed by students either individually or collectively to improve their analytical skills. Answers and feedback will be available to students for self-assessment (ILOs 1-5). In addition, students will be offered the opportunity to answer online quizzes found on Blackboard, typically at the end of the main lecture, to check their understanding of the respective week’s content (ILOs 1-3). Finally, in class and/or online polling questions will also be delivered to students via the Mentimeter response system to further check their understanding of various discussed topics (ILOs 1-3).
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 to complete their group/individual summative project assessments (ILOs 1-6).
Tasks which count towards your unit mark (summative):
Group Assignment (40% of the overall unit mark):
Students will work in small groups (of 5 or 6 members) 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 Assignment (60% of the overall unit mark):
Students will be given a 4-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. Questions are based on the material covered in weeks 13 to 22 of the unit. This assessment is related to ILOs 1-5.
When assessment does not go to plan
If the assessment does not go to plan, then the resit will consist of a single case study, no more than 2000 words, covering all aspects of modelling analytics discussed in detail throughout the semester. This will cover all ILOs 1-6.
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 Faculty workload statement relating to this unit for more information.
Assessment
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.
