MSc Business Analytics & AI for Management Curriculum
The programme consists of eight taught modules, assessed through a blend of written examinations and continuous coursework. Taught modules run across two terms: Michaelmas (September–December) and Hilary (January–April). The final Trinity term (April–August) is dedicated to your dissertation or research project.
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September-December Michaelmas Term |
January-April Hilary Term |
May-August Trinity Term |
Note: The modules listed above and below are the modules and timetable for 2026/27 and are subject to change.
Module Descriptions
Business AI deployment (5 ECTS)
Master the tools and practices that keep AI models effective, scalable, and aligned with business goals. Learn to deploy, manage, and monitor AI systems that remain robust in fast-changing environments.
How this fits your MSc journey: Equips you with essential operational skills to transform AI models into real-world solutions that deliver lasting value.
Learning outcomes:
- Deploy production-ready AI models: Use MLOps frameworks to support scalable and reliable AI deployment.
- Automate and collaborate effectively: Apply Docker and Git to manage workflows and ensure team-wide reproducibility.
- Build robust CI/CD pipelines: Streamline deployment and updates for AI systems at scale.
- Monitor performance and mitigate drift: Track AI models over time to maintain accuracy and relevance.
- Ensure ethical and legal compliance: Address risks in AI deployment through responsible design and oversight.
Business Data Mining (10 ECTS)
Turn complex data into strategic insights that drive business results. Learn to write data analytics code in R, apply advanced data mining methods, and become a sharp, ethical analytics practitioner.
How this fits your MSc journey: Equips you with core technical skills and decision-making tools vital for data-driven roles.
Learning outcomes:
- Solve business challenges statistically: Translate challenges into solvable analytics tasks.
- Master time-series forecasting: Choose and apply methods with confidence.
- Create effective analytics algorithms: Apply statistical methods in the R computational environment.
- Create business insights: Evaluate computational results and generate solutions.
- Communicate findings effectively: Share insights with clarity and business relevance.
Data Management & Visualisation (5 ECTS)
Manage and present data with clarity and impact. Learn how to work with databases, craft powerful visuals, and tell compelling stories that influence decisions.
How this fits your MSc journey: Provides essential data handling and storytelling skills for every analytics role.
Learning outcomes:
- Structure data effectively: Understand data types and models for robust analysis.
- Navigate databases confidently: Use SQL for storing, manipulating and retrieving data.
- Build visuals with impact: Apply design principles for clear impactful visualisation.
- Present complex date: Use visualisations to extract meaning from data.
- Bridge tech and business: Communicate fluently across technical and managerial teams.
Foundations of Business Analytics (10 ECTS)
Build confidence in using data to make smart, evidence-based decisions. Learn how to interpret, analyse and apply statistics to uncover insights that drive business performance.
How this fits your MSc journey: Provides the essential statistical foundation for all advanced analytics modules and real-world decision-making.
Learning outcomes:
- Think statistically: Understand core concepts from probability to regression.
- Master key techniques: Use descriptive stats, probability and regression effectively.
- Target your analysis: Identify the most relevant data to solve specific challenges.
- Uncover key drivers: Detect patterns and predictors that inform strategy.
- Make informed choices: Generate insights that lead to sound business decisions.
Digital Technologies in Practice (10 ECTS)
Bridge the gap between theory and real-world application by learning directly from leading academics and industry experts. Discover how cutting-edge research translates into actionable analytics and AI strategies across sectors.
How this fits your MSc journey: Deepens your understanding of analytics in action, preparing you to apply academic insights to business challenges with confidence.
Learning outcomes:
- Understand real-world implementation: Analyse the practical challenges of deploying analytics in diverse industries.
- Bridge theory and practice: Compare academic research with industry approaches to identify best practices.
- Explore cross-industry applications: Examine how analytics evolves across sectors and shapes operational decisions.
- Gain local insight: Evaluate analytics use in Irish industries through relevant case studies.
- Engage with experts: Build networks and gain fresh perspectives through direct interaction with global researchers and practitioners.
Generative AI for Business (10 ECTS)
Harness the power of generative AI to drive smarter business decisions. Learn to analyse data critically, use GenAI tools effectively, and uncover insights that shape policy and organisational strategy.
How this fits your MSc journey: Builds your capability to apply emerging GenAI technologies to real-world data challenges across industries.
Learning outcomes:
- Choose the right GenAI tools: Distinguish between tool types and match them to analytical tasks.
- Use GenAI in practice: Apply generative models to extract meaningful business insights.
- Align AI with strategy: Implement GenAI to support goals and inform key decisions.
- Interpret complex outputs: Assess AI-generated data with a critical, informed perspective.
- Ensure ethical use of data: Identify biases, evaluate quality, and apply responsible data practices.
Ethical and sustainable Issues for Business AI (5 ECTS)
Tackle real-world sustainability challenges with advanced data skills. Learn to analyse environmental impact, interpret global standards, and communicate insights through compelling ESG reports.
How this fits your MSc journey: Adds a future-focused lens to your analytics skillset, aligned with global sustainability goals.
Learning outcomes:
- Assess sustainability initiatives: Critically analyse actions by firms and governments.
- Model environmental impact: Use predictive tools to evaluate sustainability outcomes.
- Master ESG reporting: Craft data-driven ESG reports that meet global benchmarks.
- Drive responsible decisions: Apply analytics to make ethical, impactful choices.
- Work with real-world data: Identify meaningful insights from diverse sources.
Dissertation or Group Consultancy Project (30 ECTS)
Make a real-world impact through your final project. Whether you pursue an individual research dissertation or work on a live consultancy project in a team, you'll apply your knowledge to complex problems that matter. Either way, you’ll sharpen your analytical skills, apply research methods, and make a meaningful contribution to business and society.
How this fits your MSc journey: Culminates your learning with a hands-on or research-based project that showcases your ability to tackle complex problems with academic rigour and real-world relevance.
Learning outcomes:
- Define a focused research question: Frame your topic clearly within a wider academic and professional context.
- Evaluate key literature: Critically analyse sources to identify relevant insights and research gaps.
- Apply the right methods: Select and use appropriate theories and methodologies to guide your investigation.
- Deliver robust analysis: Gather and interpret data to support evidence-based conclusions.
- Communicate with impact: Present clear, structured findings through professional, academically sound writing or consultancy outputs.