MSc Business Analytics & AI for Management Timetable and Modules

The MSc in Business Analytics and AI for Management consists of nine taught modules and either an individual dissertation or group consultancy project. Teaching patterns vary by delivery mode, so students should review the relevant full-time, part-time on-campus or online timetable information before applying.

The modules listed below are for the 2026/27 academic year and are subject to change.

Module	ECTS
Digital Technologies in Practice (Part 1)	5
Generative AI for Business	10
Data Management and Visualisation	5
Foundations of Business Analytics	10
Digital Technologies in Practice (Part 2)	5
Business AI Deployment	5
Business Data Mining	10
Ethical and Sustainable Issues for Business AI	5
Business and AI Strategy	5
Dissertation or Group Consultancy Project	30

Technical Tools and Methods

Students develop practical experience with programming, analytics and AI-related tools across several modules. Indicative tools and methods include:

Area	Tools or Methods
Data Management and Visualisation	SQL and Tableau
Foundations of Business Analytics	SPSS and Python
Business Data Mining	RStudio
Generative AI for Business	Python, Ollama and Phi 2
Business AI Deployment	Git, Docker and MLOps
Business and AI Strategy	Python, LLMs and NVivo

Teaching Patterns by Delivery Mode

Full-Time On Campus

The full-time mode is delivered on campus over one academic year, from September to August.

Taught modules normally take place across Michaelmas and Hilary terms, with the final Trinity term dedicated to the dissertation or group consultancy project.

September-December

Michaelmas Term

January-April

Hilary Term

May-August

Trinity Term

Digital Technologies in Practice
Generative AI for Business
Data Management & Visualisation
Foundations of Business Analytics

Digital Technologies in Practice
Business AI Deployment
Business Data Mining
Ethical and Sustainable issues for Business AI 
Business and AI Strategy

Dissertation or Group Consultancy Project

Part-Time On Campus

The part-time on-campus mode is delivered over two years at Trinity Business School in Dublin. Teaching takes place on campus, with class times varying by module and semester.

The confirmed timetable will be published shortly, so applicants will be able to review the teaching pattern before the programme begins.

Students on the part-time on-campus mode should expect regular on-campus teaching at Trinity Business School, including two mandatory in-person weeks, one at the start of each academic year. In year one, students will take part in Trinity Business School and Trinity College Dublin orientation activities. In year two, students will join the block week teaching for Digital Technologies and Practice, which takes place during the first teaching week of the year.

Online

The online mode is delivered part time over two years through Blackboard Ultra. Teaching combines recorded lecture material, live online lectures, readings, online activities and discussion. Recordings are made available to support students who cannot attend live sessions.

Live online lectures are scheduled on Saturdays in Dublin/Irish time. In weeks where students are taking one module, the live lecture is normally one hour, from 2pm to 3pm Dublin time. In weeks where students are taking two modules, live lectures are normally two hours in total, from 2pm to 4pm Dublin time, with one hour per module.

Weekly recorded lecture material is normally made available in advance from Monday morning. Online students also have access to online tutorials for core technical modules and online drop-in support sessions. These sessions are designed to help students ask questions, clarify material and receive additional support with technical content.

The online mode is primarily delivered online, but students are required to attend two mandatory in-person weeks at Trinity Business School in Dublin, one at the start of each academic year. In year one, students will take part in Trinity Business School and Trinity College Dublin orientation activities. In year two, students will join the block week teaching for Digital Technologies and Practice, which takes place during the first teaching week of the year.

Applicants who may have visa or travel concerns should contact the admissions team before applying so that they understand the attendance requirement.

Assessment

Assessment varies by delivery mode and module.

The online mode has no written examinations and is assessed through continuous assessment completed virtually.

For the full-time and part-time on-campus modes, assessment may include a blend of written examinations and continuous coursework, depending on the module.

Module Descriptions

Digital Technologies in Practice (10 ECTS)

Launch your MSc journey by connecting analytics and AI theory with the realities of corporate transformation. Learn directly from industry executives, technology leaders and academics through masterclasses, interactive workshops, and company visits. You will examine cutting-edge applications, implementation barriers and operational decisions across sectors, then reflect on how these insights connect with the programme's technical modules.

How this fits your MSc journey: Provide the industry context and professional perspective needed to understand how analytics, AI and digital technologies create value in practice.

Learning outcomes:

Understand implementation challenges: Analysethe operational realities of introducing analytics and AI in organisations.
Connect theory with practice: Relate academic frameworks andprogramme learning to real industry approaches.
Evaluate market-driven adoption: Assess how changing competitive conditions influence investment in digital technologies.
Compare sector applications: Examine how analytics and AI shape decisions across different industries.
Assess Irish transformation contexts: Evaluate local innovation ecosystems, adoptionbarriers and organisational responses.
Engage with experts: Build professionalinsights and networks through informed interaction with practitioners and researchers.

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Generative AI for Business (10 ECTS)  

Move beyond using generative AI as a chatbot and learn to build systems that solve business problems. Explore model foundations, prompt engineering, tool selection, retrieval-augmented generation, memory and agentic AI. Through hands-on work with Python, Ollama and open models, you will progress from reliable prompt-based analysis to a grounded pipeline and a functioning AI agent governed by human oversight.

How this fits your MSc journey: Extend your data and analytics foundations into AI systems that reason and act, preparing you to lead responsible GenAI adoption across organisations.

Learning outcomes:

Selectappropriate AIarchitectures: Compare direct model use, RAG and agentic systems and justify proprietary or open-weight choices.
Ground AI in business data: Use prompting,structuring and retrieval techniques to produce reliable, decision-relevant outputs.
Develop adoption strategies: Prioritisegenerative and agentic AI use cases by value, risk and workforce impact.
Build an AI agent: Design a goal-directed system that plans, uses tools,retains memory and operates under human oversight.
Evaluate AI reliability: Detect hallucination, error and goal drift using structured assessment methods.
Govern AI responsibly: Address bias, privacy, data quality, intellectual property,accountability and regulatory compliance.

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Data Management & Visualisation (5 ECTS)

Build the practical data foundations behind effective analytics. Learn how to profile and prepare datasets, address missing values and inconsistencies, and manage data responsibly. You will design relational databases, use SQL to retrieve and manipulate information, and create clear visualisations and data stories in Tableau that help decision-makers understand complex findings and take action.

How this fits your MSc journey: Develop the essential data-handling, database and storytelling capabilities required across analytics roles and advanced programme projects.

Learning outcomes:

Assess data quality: Analysedatasets and models to identify issues and design appropriate structures.
Design relational databases: Applydata-management concepts to build and evaluate robust database models.
Query data with SQL: Store, join, filter, aggregate and retrieve information confidently.
Evaluate visual communication: Use cognition and pre-attentive perception principles to assess chart effectiveness.
Create impactful visualisations: Represent large datasets clearly and coherently within limited space.
Tell data-driven stories: Combine sound data management and visual design to inform decisions and drive action.

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Foundations of Business Analytics (10 ECTS)

Build confidence in using statistics to solve real business problems. Explore descriptive statistics, data types, probability, distributions, sampling, hypothesis testing, and linear and logistic regression through practical examples. Using SPSS and Python, you will learn how to select relevant data, identify key drivers and turn statistical evidence into informed managerial decisions.

How this fits your MSc journey: Establish the statistical and analytical foundation for later study in machine learning, forecasting, AI applications and evidence-based management.

Learning outcomes:

Think analytically: Explain how data-driven insight contributes to real-world business decision-making.
Understand statistical methods: Describe the principles behind the core techniques used in business analytics.
Collect andanalysedata: Apply practical approaches to sampling, measurement and statistical investigation.
Select relevant evidence: Identify the data and techniques best suited to a specific business challenge.
Identifykey drivers: Use statistical relationships to explain outcomes and make meaningful predictions.
Make informed decisions: Translate analytical findings into sound business recommendations.

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Business AI Deployment (5 ECTS)

Turn AI models into reliable systems that deliver lasting business value. Learn the foundations of MLOps and design end-to-end pipelines for deployment, monitoring and maintenance. Using Git, Docker and automated workflows, you will create reproducible projects, support scalable releases, detect model drift and address the ethical and legal responsibilities involved in operating AI systems.

How this fits your MSc journey: Connect foundational analytics and machine learning with the operational skills needed to deploy and manage production AI in real organisations.

Learning outcomes:

BuildMLOpspipelines: Design end-to-end processes for AI deployment, monitoring and maintenance.
Create reproducible workflows: Apply version control andcontainerisation using Git and Docker.
Automate deployment: Develop CI/CD practices that support reliable model releases and updates.
Monitormodel performance: Detect drift and adapt systems to maintain accuracy and business relevance.
Deploy AI responsibly: Address fairness, accountability, transparency, legalobligations and operational risk.
Scale team delivery: UseMLOps practices to improve collaboration and align AI systems with strategic goals.

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Business Data Mining (10 ECTS)

Transform business challenges into solvable statistical problems and meaningful competitive insight. Learn classification, prediction, time-series forecasting, data reduction and exploration while developing industry-standard analytics code in R. You will build and evaluate algorithms, work with large and imperfect datasets, and learn to distinguish responsible analysis from misleading or inappropriate uses of data mining.

How this fits your MSc journey: Develop the predictive modelling, programming and critical judgement required for analytics, data science and consulting roles.

Learning outcomes:

Frame analytics problems: Translate practical business challenges into discrete statistical tasks.
Apply forecasting methods: Compare time-series approaches and select techniques suited to the available evidence.
Match methods to problems: Chooseappropriate statistical tools for different business questions and data types.
Develop analytics in R: Implement algorithms using readable,reusable and efficient code.
Generate business insight: Evaluate computational results and turn them into evidence-based solutions.
Communicate responsibly: Present findings clearly whilerecognising ethical, human and societal implications.

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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.

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Business and AI Strategy (5 ECTS)

This module provides a foundation for understanding the strategic imperative of digital technologies and AI within businesses. The curriculum addresses the entire lifecycle from foundational IT and AI to the organizational complexities of Digital and AI deployment.

How this fits your MSc journey: Crucial for careers in strategic analysis and consulting, students will learn to analyse business needs, develop comprehensive digital and AI strategies, and lead digital transformation. The module employs an application and case-based approach, integrating theoretical frameworks with real-world industry examples (e.g., chatbots, VR campus, corporate aviation).

Learning outcomes:

Understand the foundational concepts of business strategy, IS, and IT, determining their interdependencies in the digital age.
Analyse the mechanisms by which key digital technologies and AI components generate and capture competitive value within organizations.
Create effective business and AI strategies by evaluating the digital competitive landscape to secure and sustain competitive advantage.
Evaluate and differentiate various digital business and platform-based strategies and justify their strategic application to diverse organizational contexts.
Formulate executable strategies for leading digital transformation while analysing the necessary business elements of foundational IT tools and AI models.
Analyse the structural impact of AI on the future of work and design coherent strategies for workforce management and talent development.

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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.