Business Analytics and Intelligence, offers a comprehensive exploration into the sophisticated landscape of data-driven decision-making within modern businesses. It combines the critical disciplines of Business Analytics (BA) and Business Intelligence (BI), providing students with the theoretical foundations and practical skills necessary to excel in these dynamic fields.

1. Apply frameworks and processes involved in extracting, transforming, and loading data to derive actionable insights.
2. Synthesise knowledge from business, technology, and data science to create new insights and develop innovative solutions in business intelligence.
3. Apply advanced analytical techniques.
4. Assess and utilise various analytical tools and methodologies to conduct thorough business analyses in a managed approach.
5. Demonstrate ethical and responsible use of data in analytics, considering data privacy, security, and the implications of business intelligence for sustainability.

This module provides an in-depth exploration of advanced data management techniques and analytics. Students will gain comprehensive knowledge of both relational database management systems (RDBMS) using SQL and big data databases, with a focus on NoSQL document databases. The course is designed to equip students with the skills necessary to manage, analyse, and derive insights from large datasets.

1. Design a relational database schema and develop a relational database.
2. Design and develop a database that can be deployed in a non-relational database environment.
3. Develop and implement database solutions using SQL and NoSQL languages.
4. Critically analyse and evaluate the differences between relational and non-relational databases.
5. Identify and critically evaluate emerging trends and technologies in data management.

This module will provide the student with an introduction to the statistical and computational techniques which underpin business analytics.

1. Critically examine and apply foundational principles of statistical techniques.
2. Critically select, evaluate, and apply appropriate descriptive statistical techniques.
3. Identify, apply and evaluate appropriate techniques and procedures in inferential statistics.
4. Design, implement and critically appraise quantitative forecasts and predictions for business problems.
5. Evaluate and integrate statistical approaches for solving problems in business analytics.

This module introduces the student to different research methods, approaches and philosophies. In addition, students develop the skills to identify a research question and critically review academic and/or professional literature to address that question.

1. Evaluate different research methods, approaches and philosophies.
2. Identify a problem and define an original research question.
3. Evaluate the significance of data to address a research question.
4. Develop a research proposal and appropriate legal and ethical application.
5. Operate independently and responsibly, and take positive and active ownership of their learning.

This module is designed to equip students with essential programming skills and knowledge required for effective data analysis in a business context. Students will gain a solid foundation in problem-solving using programming languages and learn to automate tasks through scripting languages. The module focuses on practical applications while enabling students to streamline business processes through automation. By the end of this module, students will be proficient in leveraging programming to enhance their analytical capabilities and drive data-driven decision-making in a business environment.

1. Demonstrate proficiency in a programming language commonly used in business analytics.
2. Critically evaluate and efficiently execute tasks while leveraging problem-solving techniques and programming skills to design and implement automated processes.
3. Set up and configure a comprehensive integrated development environment and toolchain for development.
4. Develop and synthesise effective algorithms to address and solve business problems.
5. Integrate and combine modular programs to design and implement advanced, functional applications, applying critical thinking and problem-solving skills.

This module explores the role of Artificial Intelligence (AI) and emerging technologies in shaping the future of business analytics. The module covers a range of AI techniques, including machine learning, natural language processing, generative AI and automation tools. This module investigates how these technologies can be applied to solve business challenges. It also examines emerging technologies such as blockchain, IoT, and augmented reality (AR), providing students with a comprehensive understanding of how these innovations can be leveraged for competitive advantage in a data-driven world.

1. Critically analyse the role of AI in optimising business decision-making processes and evaluate its implications for organisational strategy.
2. Apply AI techniques such as machine learning, deep learning, generative AI and natural language processing to real-world business problems.
3. Critically evaluate the transformative potential of emerging technologies such as blockchain, IoT, and augmented reality on business analytics and decision-making frameworks.
4. Design and implement AI-driven solutions using appropriate tools and methodologies.
5. Assess ethical, legal, and societal implications of AI and emerging technologies in business contexts.
6. Synthesise strategic insights to evaluate and leverage emerging technologies to transform business operations and customer experiences.

The objective of this module is to equip postgraduate students with advanced skills in creating, analysing, and critically evaluating data visualisations, with a particular emphasis on integrating AI-driven techniques, and applying ethical and sustainable practices to effectively communicate data-driven insights. Students will learn to transform complex data sets into clear, insightful visual representations and dashboards while applying key design principles and best practices.

1. Apply advanced data visualisation techniques using various tools and software to transform complex data sets into clear, insightful visual representations and interactive dashboards.
2. Critically analyse and evaluate the effectiveness of the different data visualisation tools and methods.
3. Apply universal and key design principles and best practices in data visualisation, including the integration of AI tools to enhance visualisations.
4. Effectively communicate, to tell compelling stories and complex findings to diverse audiences, including non-technical stakeholders.
5. Assess ethical considerations in data visualisation, ensuring that visual representations of data are accurate, honest, and responsibly presented, avoiding misleading or deceptive practices.

This module aims to develop students' knowledge and understanding of the process and practice of IT strategy and governance within companies. Students will critically assess and examine a range of topics, theories and issues pertaining to IT strategy covering the delivery of value with IT, IT business partnerships, IT enabled innovation and IT strategy execution.

The module will also explore how corporations deal with critical issues such as AI governance, compliance with AI regulations (e.g., EU AI Act, GDPR), ethical considerations, and the integration of AI-driven systems for decision-making and risk management. The module will, above all, give students on this course an overall, strategic and organisational context within which to better understand the contribution of IT strategy and governance to achieve business value.

1. Evaluate and systematically appraise the mechanisms through which information technology enhances business value.
2. Examine and analyse the key concepts relating to how an IT function needs to work with business to effectively deliver digital products and services to the organisation.

3. Critically evaluate and synthesise the key challenges organisations face in successfully innovating with information technology.
4. Critically assess how to design and implement the necessary guardrails within which technology is adopted and used.
5. Evaluate and interpret corporate, industry, and regulatory governance frameworks and the necessary ethical considerations.

This module provides an opportunity for Msc . Business Analytics students to undertake an independent research project focused on applying business analytics concepts and tools to address real-world business challenges. Building on the foundation established in the Research Methods module in Semester 1–where students identify a problem and develop a project proposal—this module covers the subsequent stages of data collection, analysis, solution implementation, and evaluation. Emphasis is placed on aligning project outcomes with current industry trends and meeting stakeholder needs, ensuring the research's practical relevance. The primary goal is to produce a thorough research report that reflects the student's ability to apply academic knowledge and analytical skills, generating insights and solutions relevant to the business or enterprise sector. Ethical considerations, adherence to industry best practices, and professional standards are integral to the research process, fostering responsible and impactful business analytics solutions. The expected outcomes include a detailed research report and an associated artefact, demonstrating the student's practical application of analytical skills, academic knowledge, and ethical considerations in a real-world business context.

1. Formulate and propose relevant research question, clearly defining project objectives and methodologies appropriate to business analytics.
2. Critically evaluate the state of the art in business analytics through a comprehensive literature review, identifying gaps and novel contributions.
3. Design, develop, and implement an advanced analytical solution, applying state-of-the-art tools and techniques to address complex research questions.
4. Critically assess and evaluate the implemented solution’s effectiveness using key performance indicators and stakeholder feedback.
5. Identify and propose potential areas for future research, innovation, or practical application based on project outcomes.
6. Critically evaluate and apply ethical principles in data management and research, demonstrating advanced professional understanding.
7. Present and defend research findings through a viva voce, artefact demonstration, or written report.
8. Demonstrate effective communication, teamwork, and leadership in managing time and resources during project execution.