MSc Business Analytics 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 |
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Note: Modules offered each academic year are subject to change. The modules listed above and below are the modules and timetable for 2025/26.
Module Descriptions
Analytics in Practice (Workshops) (5 ECTS)
Bridge theory and practice through real-world exposure to analytics and AI. Learn about the latest advancements in analytics and AI through guest lectures and discover how industry professionals are applying these technologies in real-world scenarios.
How this fits your MSc journey: Provides a deeper understanding of applied analytics by integrating theoretical knowledge with real-world practice.
Learning outcomes:
- Identify barriers to implementation: Examine real-world challenges of applying analytics in industry.
- Bridge research and practice: Connect academic research with applied analytics.
- Track industry trends: Understand how analytics applications are evolving across sectors.
- Insights from experts: Learn from experts in academia and industry.
- Analytics in Ireland: Evaluate how Irish industries are leveraging analytics.
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.
Advanced Topics in Analytics (5 ECTS)
How this fits your MSc journey: Strengthens your ability to solve real-world business problems using prescriptive analytics techniques.
Learning outcomes:
- Apply optimisation techniques: Find the best solutions to multi-criteria challenges.
- Simulate scenarios: Model uncertainty and test outcomes using “what-if” analysis.
- Build analytical models: Create structured spreadsheet tools for complex decisions.
- Use spreadsheets strategically: Build advanced tools for analytics and planning.
- Evaluate performance impacts: Understand the outcomes of alternative action.
- Think like a strategist: Use analytics to guide long-term decisions.
Big Data and AI in Business (10 ECTS)
Design future-ready data strategies that embrace emerging technologies. Understand how AI and Big Data can transform business, and lead projects that deliver real impact.
How this fits your MSc journey: Prepares you for leadership roles by blending technical insight with strategic vision.
Learning outcomes:
- Decode the data ecosystem: Grasp how Big Data is created, stored and used.
- Navigate AI in business: Understand frameworks, tools and real-world applications.
- Plan for the future: Anticipate tech trends and build adaptive data strategies.
- Lead data projects: Define, manage and deliver impactful AI initiatives.
- Make smart tech choices: Select the right tools to meet business need.
ESG Analytics (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.
Social Media Analysis (10 ECTS)
Turn social media data into business intelligence with cutting-edge analytics techniques. Learn to analyse emotion, sentiment, and network patterns across text, image and video.
How this fits your MSc journey: Equips you with high-demand skills in digital analytics, essential for roles in marketing, strategy and data science.
Learning outcomes:
- Understand online behaviours: Analyse how people and organisations use social media.
- Mine key signals: Apply analytics to detect sentiment, emotion and trends.
- Decode multimedia data: Work with images, text and networks for deeper insights.
- Track community dynamics: Use graph theory to explore digital relationships.
- Generate digital intelligence: Build actionable insights from social data.
Dissertation or Research Project (30 ECTS)
Showcase your expertise by tackling a real-world challenge or exploring an area of personal interest. Apply everything you’ve learned to solve real problems or generate new knowledge.
How this fits your MSc journey: Serves as the capstone experience, demonstrating your readiness for high-impact roles or further study.
Learning outcomes:
- Conduct rigorous research: Review literature and define a strong research question.
- Apply proven methods: Choose and justify effective research techniques.
- Integrate your learning: Use insights from across the programme to inform your work.
- Communicate with clarity: Write persuasively and meet academic or industry standards.
- Deliver real value: Complete a substantial, meaningful project with measurable outcomes.