MSc Business Analytics: Operational Research and Risk Analysis

This course unit offers students the opportunity to apply their cumulative knowledge in a practical, real-world setting. This may be delivered through engagement with external partners or through approaches designed to mirror real-world professional contexts. A central pillar of this unit is teamwork; students work in collaborative groups to tackle complex, data-intensive consultancy briefs that reflect current industry demands.
The project is divided into two distinct components: First, students work in teams to address a complex business challenge. Then, each student completes an individual research project that critically analyses the team’s findings.

Equip students to apply advanced analytics and AI concepts and techniques to real-world business problems in a consultancy context.

Develop research-informed approaches to formulating recommendations for stakeholders, including literature review, institutional analysis, and data analytics methods.

Foster skills in collaborative consultation projects, including project management, leadership, and inclusive communication.

Develop proficiency in stakeholder engagement from initial scoping through final briefing, including communicating findings and recommendations.

Develop ethical practices, including decision-making for AI-driven solutions. 

Introduction to the consultancy project’s objectives, expectations, deliverables, and assessment criteria.
Overview of project management principles
Effective group dynamics.

Understanding the consultancy role
Exploration of consulting skills: stakeholder engagement, communication, and problem-solving.
Discussion of consulting methodologies and ethical considerations in consultancy projects.

Problem framing and project scoping

Enquiry-based learning
Translating the business problem into a data analytics strategy.
Designing data analytics roadmap, evaluation and deployment.

Conducting literature review

Conducting institutional analysis

Impact: practical and managerial impact

Presentation skills and influencing stakeholders workshop
Techniques for delivering a persuasive presentation: Storytelling, visual aids, and addressing key stakeholders.
How to tailor presentations to influence various stakeholders effectively, highlighting value and impact.

Research process for the business analytics problem.

This course will consist of lectures (20 hours), seminars (10 hours), supervision (6 hours), and guided independent study (564 hours).

This course will consist of lectures, seminars/ workshops.
In the lectures, students learn various techniques required for the consultancy project, such as project management and stakeholder management.

During the seminar, they apply those techniques to their group project.

Students are expected to work together during their independent learning time to apply data analytics methods learned from the programme to their project case, with feedback from their project supervisor.

Students are also expected to apply academic theories and approaches to their individual research during the independent learning time.

6 hours of supervision will include group meeting as well as individual meetings as needed to ensure students are sufficiently guided in the development of the assessment.

Apply business analytics and AI approaches to authentic stakeholder business problems.
Critically evaluate relevant business analytics literature to contextualise findings within recent developments in business analytics.

Design a robust research design, including AI-driven analytics to address complex research or business problems.
Critically evaluate findings to develop feasible data-driven recommendations and actionable insights. 
Critically assess limitations in data, methodologies, and findings, identifying areas for further exploration.

Engage effectively with stakeholders to elicit requirements and manage expectations.
Produce a professional consultancy report integrating data analytics findings, recommendations, and implementation planning.

Communicate complex data analytics findings and recommendations clearly to professional stakeholders.

Group Consultancy Presentation 10%
Group Consultancy Report 50%
Individual Research Report 40%
 

Written feedback online within 15 working days of the submission deadline. 

Saunders, M. N. K., Lewis, P., & Thornhill, A. (2019). Research methods for business students (Eighth edition.). Pearson Education Limited.

S. Guido, A. Müller. Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media, 2016.

Maylor, H; Turner, N. (2022) Project Management. Pearson.