MSCI4110: Business Data and Intelligence

• Terms Taught: Michaelmas 
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

MSCI4120: Managing Uncertainty in Business

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

In practice, business decisions are rarely made with complete information or perfect predictions of future events, and therefore an understanding of uncertainty is crucial. This module provides an introduction to important Business Analytics techniques for modelling and managing uncertainty. The aims of the module are to:

• Introduce fundamental techniques from Business Analytics and show how these can be combined with probabilistic concepts to create models of business processes operating under uncertainty
• Develop the computing skills necessary to transform data into mathematical models and use these to gain insight into business scenarios
• Explore realistic case studies in order to gain an understanding of how the effective modelling of uncertainty can enable improved decision-making policies and better performance analysis

Educational Aims

Upon successful completion of this module students will be able to…

1. Explain and demonstrate understanding of specific quantitative techniques for modelling business problems under uncertainty
2. Apply the taught techniques to case studies based on real-world scenarios
3. Describe the possible strengths and limitations of the taught techniques when addressing specific issues and problems in organisations
4. Use appropriate spreadsheet software to create mathematical models and implement the taught techniques
5. Work effectively in teams and develop the communication and presentation skills needed to explain the application of the taught techniques to a specific problem related to Business Analytics

Outline Syllabus

The module will begin with an overview of Business Analytics and its role within the context of businesses, organisations, stakeholders, objectives and the need to solve challenging problems under uncertainty. A case study will be introduced in which uncertainty is an important element. In order to understand the problem to be addressed within this case study, we will review important concepts from probability theory, including basic probability rules, random variables and standard discrete and continuous distributions. We will then introduce simulation as a means of imitating the behaviour of a real-world system in order to gain insight into the consequences of implementing a particular decision option or investigating the effect of a possible change to the system (among other possible uses). An understanding of the probability concepts from earlier in the module will be necessary in order to develop a simulation model. We will also discuss how to analyse the results from simulation, including the use of confidence intervals to interpret the range of possible outcomes of a random experiment. A second case study will be introduced and this will involve the use of a queueing model. Queueing systems have been widely studied in management science and we will review the important characteristics of such models, how they rely upon random distributions in order to model (for example) customer inter-arrival times and service times, and how to use analytical methods to evaluate their performance. The final part of the module will cover decision analysis. This will include discussion of the different decision criteria that might be adopted by decision-makers in practice and how utility theory can be used to model different attitudes to risk.

Assessment Proportions

There will be two summative assessments for the module. The first is a group assessment, related to the first case study. Students will need to work in small groups on a task that involves modelling and decision-making under uncertainty. An assessed presentation will need to be given, approximately halfway through the module. The second assessment is a written exam in which students will need to demonstrate their understanding of all of the topics taught during the module. In addition to these summative assessments, formative assessment will be conducted through exercises covered in tutorials and workshops and also through online quizzes provided on Moodle. These exercises and quizzes will not contribute to students’ summative marks for the module, but will provide students with opportunities to assess their own understanding of the relevant skills and techniques.

MSCI4160: Computing for Business Decision Making

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

Using Python, this module develops foundational computer programming skills and shows students how programming can be used to tackle problems in operational research and business analytics. Special emphasis in particular will be given to implementing heuristic optimisation algorithms. These are useful in themselves in that they can be used to tackle a range of difficult decision-making problems such as vehicle routing. Implementing such algorithms is additionally an excellent exercise to develop programming skills and to demonstrate the utility of programming itself.

Educational Aims

Upon successful completion of this module students will be able to…

1. Understand the concept of an algorithm and be able to analyse the complexity of simple algorithms
2. To write Python programs using imperative programming features and use basic Python data structures such as lists and dictionaries.
3. Use Python to implement heuristic optimisation algorithms
4. Write code which is reusable code by leveraging language features such as modules and docstrings
5. Use different coding environments such as Spyder and Jupyter, and software engineering tools such as debuggers

Outline Syllabus

The module will begin by introducing algorithms and algorithmic complexity. Alongside this, the students will begin to learn imperative programming in Python. Once equipped with this knowledge, the students will implement some simple algorithms and mathematical models. The part of the second course will introduce some important combinatorial optimisation problems which arise in operational research such as the travelling salesman problem. They will learn about the computational complexity of solving these problems which will motivate the need for heuristic algorithms for solving them. The students will implement some of these more complicated algorithms, covering any advanced data structures that are required to do this. The final part of the course will concern additional Python features (such as modules and file operations) and software engineering tools like debuggers which are required to write more sophisticated programs. Particular emphasis will be put on the programming principles of modularity and reusability here. The module will culminate in an individual coding project which will require students to develop a command line decision support tool related to a combinatorial optimisation problem.

Assessment Proportions

Teaching on the module will be through a series of lectures, tutorials and computer workshops. There will be two lectures per week (1 hour each) during each of the teaching weeks. Most of these lectures will be based in computer labs to enable the students to complete short coding exercises as new concepts and programming constructs are introduced. In addition, students will be expected to attend 2 hours of computer workshop or tutorial depending on the content being covered that week. The tutorials will be used to hold discussions and practice exercises related to the theoretical course content. The computer workshops will be used for students to complete longer programming exercises. The module will use two computer-based assessments to assess the help assess the first two parts of the module. The final assessment will be an individual coding project which will require knowledge and skills developed over the whole module.

MSCI4199: Professional Development for Business Analytics

• Terms Taught: Full year 
• US Credits: 10
• ECTS Credits: 20
• Pre-requisites: None

MSCI4202: Foundations in Operations Management

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: College-level Maths

Course Description

This module aims to introduce the fundamental principles of Operations Management, a core managerial discipline for understanding how all kinds of organisations, in manufacturing and service sectors, create and deliver products and services. Students will explore the diverse processes that underpin the modern world, such as transportation, retail, goods production, and the provision of medical and educational services. The course establishes a foundational understanding of the strategic role of operations, the design of operations, the analysis and resolution of operational problems like congestion, inventory shortages, and quality control, and operations improvement. Grounded in practical case studies, it covers qualitative and quantitative aspects of operations management, highlighting its strong connections to other managerial disciplines and its relevance to any future managerial career.

Educational Aims

Upon successful completion of this module students will be able to:

1. Describe and differentiate between key operations management concepts relevant to the strategic purpose, design and management of processes for goods and services.
2. Identify potential operational problems that may occur in the creation and delivery of goods or services across various operational settings.
3. Apply quantitative and qualitative approaches to analyse operational scenarios and formulate evidence-based recommendations for improvement.

Outline Syllabus

Operations functions typically employ the majority of staff in an organisation and manage most of the assets. Operations deliver what the organisation provides or sells: cars, health-care, legal advice, transportation, education, and so on. The module examines how different operations can be designed to effectively deliver the strategic objectives of their organisations, depending on the nature of demand and the priorities of customers or users. It also introduces techniques for designing and diagnosing problems with particular aspects of the operation and its supply chain, and how to improve the operation to overcome these problems. Some of the algorithmic techniques introduced are commonly embedded within enterprise software systems used to manage and integrate business processes. Operations are also considered in the context of sustainability and other aspects of social purpose. The main topics are:

• The Operations function and its strategic role;
• The design and management of supply chains;
• Process thinking and process design;
• Demand forecasting and capacity analysis;
• Inventory analysis;
• Quality management and process improvement;
• Enterprise resource planning & lean production;
• Project planning and control;
• Operations sustainability and humanitarian operations.

Assessment Proportions

The teaching and learning strategy for this module is designed to provide first year students with a fundamental understanding of core operations principles essential for all organisations. The module introduces both qualitative and quantitative analytical techniques applicable across manufacturing and service sectors, enabling students to grasp how organisations create and deliver value. This contributes to broader module aims by developing foundational business knowledge, analytical thinking, and problem-solving skills relevant to any future managerial career.

The assessment is constructively aligned to learning outcomes and comprises group coursework (50%) and a final examination (50%). The group coursework enables studies to collaboratively analyse an operational scenario, apply relevant concepts and propose evidence-based recommendations. The final exam assesses students’ understanding of key operations management principles and their ability to identify operational issues. Formative assessment is integrated throughout the module, with regular informal feedback provided during seminar exercises and class quizzes to support student learning and preparation for summative tasks.

MSCI5100: Forecasting and Machine Learning

• Terms Taught: Full year 
• US Credits: 10
• ECTS Credits: 20
• Pre-requisites: College-level Maths

MSCI5142: Spreadsheet Modelling Techniques

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module aims to teach students how to handle and manipulate spreadsheet datasets, over multiple sheets and files, analyse data, create decision support models to influence decisions, evaluate and debug spreadsheet models, create VBA macros to automate tasks, and develop tools to investigate business problems.?

Managing, visualising and analysing data, and integrating this data into decision support models is an essential skill in today’s data-driven organisations.

Educational Aims

Upon successful completion of this module students will be able to:

1. To understand how to build a dynamic, well-structured spreadsheet model?
2. To understand how to use a wide range of Excel functions to handle, filter and visualize data of different types
3. To know how to produce effective charts and data summaries?
4. To understand how models can support decision making

Outline Syllabus

General modelling: functions, data structures, data handling, data manipulation and filtering, charting, and basic tool use.? Introduction to VBA: macro recording, programming, code structures, debugging and model automation. Case-study modelling: Hospital patient flows. Simulation modelling of a queueing system. Data handling, visualisation and dashboard design. All involving VBA support. Advanced Tools: optimisation, Solver using VBA, custom userforms and ActiveX?controls.

Assessment Proportions

A weekly lecture with a demonstration of a spreadsheet model/analysis of a dataset. An accompanying weekly computer lab session, supported by full instructions, additional tasks, and a completed model. Two pieces of assessment (individual): Students must build a model to analyse a dataset and provide analytical support for a business case.

MSCI5351: Project Management: Theories, Tools and Techniques

• Terms Taught: Michaelmas 
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

MSCI6281: Global Supply Chain Management

• Terms Taught: Full Year
• US Credits: 10
• ECTS Credits: 20
• Pre-requisites: Requires some previous operations management study

MSCI6303: Smart Data and AI Systems for Business

• Terms Taught: Michaelmas 
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module aims to provide students with a critical and practical understanding of how data analytics and artificial intelligence (AI) are shaping modern business practices and organisational strategies. Students will explore the lifecycle of business data—from collection and governance to analytics and insight generation—and how AI technologies, such as machine learning, deep learning, and generative models, can be applied to solve real-world problems and create value. Through lectures, case studies, and hands-on work with accessible AI tools and platforms, the module cultivates both technical and strategic literacy. It fosters students' ability to think critically about the deployment and ethical implications of AI systems in business contexts, as well as the organisational challenges of managing AI-driven change. The module also supports the development of key transferable skills including:

• Analytical and systems thinking
• Problem-solving using data-driven approaches
• Digital fluency with AI/low-code platforms
• Academic and business writing and communication
• Ethical reasoning and reflective practice

By integrating theoretical insight with practical application, this module prepares students to engage thoughtfully and competently with smart data and AI systems in their future careers as managers, analysts, or entrepreneurs in a digital economy.

Educational Aims

Upon successful completion of this module students will be able to…

1. Critically evaluate the role of data and AI technologies in transforming business processes, decision-making, and strategy across industries.
2. Assess and apply appropriate methods for data collection, preparation, and governance to ensure reliable inputs for AI-driven analysis.
3. Demonstrate understanding of key AI techniques—including machine learning, deep learning, and generative models—and their application to real-world business challenges.
4. Use low-code/no-code tools (e.g. KNIME, GPT-based platforms, n8n) to design and implement AI-powered solutions for business problems.
5. Analyse the ethical, legal, and organisational implications of deploying AI systems, including issues of bias, explainability, and risk.
6. Communicate findings and solutions effectively through written reports and visualisations.

Outline Syllabus

This module explores how data and artificial intelligence (AI) technologies are shaping the future of business. It introduces students to the foundational role of data in modern organisations, emphasising the importance of data quality, governance, and ethical collection as critical enablers of AI success. Students will engage with real-world examples and case studies to understand how businesses across sectors are using smart data and AI to innovate, automate, and improve decision-making. Building on this foundation, the module progressively introduces students to a range of AI techniques, including machine learning, deep learning, and generative models such as large language models. Students will work with accessible tools and platforms (e.g. KNIME, GPT-based systems, and low-code automation environments) to design, test, and refine AI solutions to practical business challenges. These hands-on exercises are complemented by critical discussions about the social, legal, and organisational risks associated with AI—including bias, transparency, and accountability. The syllabus encourages a reflective approach to technology, foregrounding issues of fairness, power, and inclusion. Students are invited to consider how algorithmic systems may reinforce historical inequities, and how more inclusive approaches to data and AI design might support socially responsible innovation. Through hands-on project work and critical reflection, students develop digital fluency, problem-solving skills, and the capacity to evaluate AI tools not just for their technical functionality, but for their broader impact on people, organisations, and society.

Assessment Proportions

Formative learning is supported through weekly lectures, labs, in-class discussions, and peer feedback on project ideas. Students are encouraged to engage in inquiry-based learning and draw on diverse perspectives and contexts in their analyses. The module is structured to accommodate a variety of learning styles and includes visual, practical, and discussion-based activities. Assessment is constructively aligned to the module learning outcomes. It includes an open-book lab project (65%) structured as a business problem, and a take-home paper (35%) designed to reflect key learnings. These assessments allow students to demonstrate learning through both applied and theoretical perspectives, and to apply knowledge in creative, real-world contexts. Regular feedback opportunities are embedded through project milestones and lab check-ins.

MSCI6352: Negotiation Skills

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module aims to give the students an opportunity to apply negotiation theory in a simulated, experiential negotiation game, developed by the module convenor. It will teach through exposure and experience the negotiation concepts of proposing, signalling, bargaining, trading, conflict resolution and collaboration. It will also involve managing communication, cooperation, running meetings, agreeing scope and contracts, both within a team and between teams. It will also involve using a decision support model (DSS) in a decision-making context. The role and importance of negotiation in today’s business world is significant, in terms of signing contracts, working in teams, dealing with issues and conflict, managing projects, dealing with pressure, time, and deadlines. It also provides experience with the using data and a DSS model in a decision-making situation.

Educational Aims

Upon successful completion of this module students will be able to…

1. Understand the reality and logistical problems of coordinating communication.?
2. Work well within a team, and liaise with other teams with differing priorities.?
3. Develop solutions to complex organisational problems.?
4. Project manage and conduct a negotiation with another organization.?
5. Understand the role of data and Decision Support Systems (DSS) in a negotiation.
6. Reflect on a negotiation process and understand the dynamics of decision making.

Outline Syllabus

There will be very few formal lectures on the module, because much of this time will be devoted to playing the Crossbay Negotiation Game. This is a health-service based simulation created by the module convenor. There will be lectures on 'negotiation skills' and on decision supporting systems (in Excel). There will also be various practice negotiation sessions, with formative feedback and guidance sessions.? There will be two formal negotiations: a preliminary and a final negotiations. The students will be assessed on their behaviour in the final negotiation (team mark), the quality of the contract they sign (team mark) and a reflective report on the negotiation process (individual).

Assessment Proportions

The module attempts to teach the practical aspects of negotiation through experiential learning, feedback and reflection (not simply negotiation theory). There will be a series of introductory lectures on negotiation theory but the majority of the module is given over to playing the Crossbay Negotiation Game. Students will be assigned to a team of 3 and will be required to negotiate a contract for their organisation within a game of 3 teams (a tripartite negotiation). The negotiations will culminate with a final assessed negotiation which is 1 hour 30 minutes long, in which they will be required to sign a contract. The final piece of assessment is a reflective report in which the student describes and reflects on the game and attempts to understand the dynamics of negotiation, and draw insights into their own behaviour and how the theory connects with real-life experiences.