This module is designed to introduce you to essential skills and resources that will support your studies throughout the programme. Delivered through a blend of taught sessions and directed learning, it starts with an induction learning pack featuring recommended reading to help you prepare for the core modules ahead. You’ll have access to LinkedIn learning resources, tailored to boost your foundational knowledge in areas like statistics, SQL, business acumen, and programming.

These self-paced resources allow you to choose topics that suit your background, ensuring you build a solid foundation in areas you may be less familiar with, such as statistical analysis, SAS, and AI in the service sector. Throughout the module, you’ll gain an understanding of key metrics, the competitive business environment, and basic SQL for data extraction. This introductory module establishes the core knowledge in analytics, business, and programming, setting you up for success in more advanced modules.

This module introduces you to data mining, focusing on extracting valuable insights from vast datasets to support informed decision-making across sectors like marketing, security, and bioinformatics. You’ll gain a comprehensive understanding of data mining methods, including association analysis, clustering, and predictive modelling (regression, decision trees, neural networks), using industry-standard tools like SAS Enterprise Miner, BASE SAS, and open-source alternatives such as Weka and R.

Exploring real-world applications—from fraud detection to trend analysis—you’ll learn to apply the appropriate techniques for specific problem domains. Practical exercises and coursework will strengthen your skills in data processing, model evaluation, and interpretation, preparing you to draw actionable insights. Advanced topics include time series analysis, text mining, and explainable AI, all while covering essential privacy and ethical considerations.

By the end of the module, you’ll have a solid grounding in data mining tools and techniques, enabling you to address diverse data-driven challenges effectively.

This project module offers an initial, self-directed study in data analysis, where you’ll work with a specified dataset to address a defined research problem. Building on core techniques and skills acquired in prior modules, you will evaluate, select, and apply computational techniques for data analysis and knowledge extraction, critically assessing your approach and results. The project involves synthesising or using public datasets and demonstrates your understanding through a written report (approximately 5,000 words) covering problem statement, literature review, methodology, results, discussion, conclusion, and future work.

Support includes introductory sessions on research methods and one-person project skills, as well as dataset synthesis techniques. This project may serve as a preliminary study for a later, more advanced project tackling a similar research problem.

This foundational module introduces you to the essential concepts of Business Intelligence (BI) and Analytics, Sustainability, and Ethics, preparing you to navigate the evolving data analytics landscape with a strong ethical and sustainability focus. You'll gain an understanding of the architecture and application of BI systems in organisations, exploring critical topics like business metrics, types of data, dashboard design, and aligning data strategies with business goals.

A key component of the module is the integration of sustainability into data analytics, emphasising how data can support Sustainable Development Goals (SDGs) and promote environmental and social responsibility. Ethical considerations, such as data privacy, security, and bias, are also explored, providing a comprehensive view of responsible data usage.

By the end of the module, you’ll be able to build a compelling business case for BI system implementation, develop and present BI proposals professionally, assess the environmental and social impacts of business decisions, and apply ethical principles to real-world data analytics scenarios.

This module provides you with eight hours of engaging training to become carbon literate, empowering you with the knowledge and motivation to reduce carbon emissions on personal, community, and organisational levels. Carbon literacy involves understanding the carbon costs and impacts of everyday activities and learning actionable strategies to reduce these emissions.

Throughout the module, you’ll cover essential topics such as climate science, calculating your carbon footprint, climate justice, and effective communication about sustainability. You’ll also explore the benefits of carbon literacy accreditation, a valuable asset for graduate employability in today’s environmentally conscious job market. The module features interactive, enjoyable activities, including quizzes and group discussions, to solidify your understanding and foster meaningful engagement with climate issues.

The Fundamentals of Big Data and Infrastructure module offers a foundational understanding of big data and its supporting infrastructure, focusing on two key areas: data warehouse design and big data analytics.

In the first part, you’ll learn about data warehouse architecture and design principles, including dimensional and entity relationship modelling, tailored to meet specific business requirements. This section builds your skills in designing robust data storage solutions and understanding the essential role of data warehouses.

The second part focuses on big data analytics, covering methods for collecting, storing, and analysing vast, unstructured datasets. You’ll gain hands-on experience with advanced tools such as Hadoop Distributed File System (HDFS), Apache Spark, and GraphFrames, as well as cloud-based parallel computing. The module also addresses data ethics, equipping you to apply ethical standards in handling data.

By completion, you’ll be prepared to tackle big data challenges and make informed, ethical, data-driven decisions within scalable infrastructures.

This module equips you with essential skills in statistical analysis and research methodology. You’ll explore both quantitative and qualitative research approaches, including laboratory evaluation, surveys, case studies, and action research, gaining practical experience with a statistical application like SAS Studio.

Statistical content covers sampling methods, probability, distributions, and key statistical tests (e.g., t-tests, ANOVA, Chi-Squared). You’ll learn to design and conduct data analysis, interpret results, and critically assess published research findings. The research methods component focuses on defining research problems, choosing appropriate methodologies, conducting literature reviews, and managing projects, with an emphasis on ethics and project funding.

Through practical assignments, you’ll refine your ability to select and justify research methods, draft research proposals, and effectively communicate findings. By the end of the module, you’ll be well-prepared for independent research, with a solid grounding in statistical techniques and a critical approach to data-driven decision-making.

This final data analytics project offers you the opportunity to conduct an in-depth, self-directed study, applying and expanding the skills gained throughout your course. You'll address a novel data analysis problem by using advanced techniques, creating a new method for data interpretation, or developing innovative ways to present data to support decision-making. Your work will culminate in a comprehensive report of around 7,000 words, covering everything from problem identification and literature review to methodology, results, and future applications.