Data Science Applications (Conversion)

In this module you will develop the fundamental programming and development skills required to create simple data-driven applications. Designed for students with no prior technical experience, the module introduces programming concepts through practical activities that build confidence and capability. You will learn how to work with data, write basic scripts, solve problems systematically and use code to collect, clean, analyse and present information. Practical exercises and real-world case studies will demonstrate how data-driven solutions are applied across different sectors. By the end of the module, you will be able to design, develop and explain basic applications that support data science activities.

In this module you will build confidence in the mathematical and statistical concepts that support modern data science practice. Through practical examples and application-focused learning, you will explore probability, statistics, introductory calculus, and key linear algebra concepts such as vectors and matrices. The module emphasises how mathematical thinking is applied to real-world business, organisational and social challenges rather than abstract theory. You will learn to formulate data-related problems, select appropriate mathematical techniques, interpret analytical outputs and evaluate results critically. By developing quantitative reasoning skills, you will gain a strong foundation for further study in data science and machine learning.

In this module you will develop a practical understanding of machine learning and its application to real-world challenges across diverse sectors. You will explore how machines learn from data, covering supervised, unsupervised, generative and agentic approaches while gaining experience with industry-standard tools such as PyTorch. The module introduces the complete machine learning lifecycle, including data preparation, model selection, training, evaluation and deployment considerations. Through applied projects and case studies, you will learn to critically assess model performance and suitability. Ethical, societal and professional responsibilities associated with machine learning and automated decision-making are integrated throughout your learning experience.

In this module you will gain a comprehensive introduction to data analytics and visualisation, developing the skills needed to transform data into meaningful insights. You will work through the complete analytics workflow, including data collection, cleaning, transformation, exploration and interpretation. The module emphasises critical thinking, statistical reasoning and ethical decision-making when analysing and presenting information. You will learn how visualisation techniques can reveal patterns, communicate findings and support evidence-based decisions for different audiences. Through hands-on activities using industry-standard tools and technologies, you will build practical experience in creating clear, accurate and impactful analytical outputs across diverse professional contexts.

In the module you will undertake an independent capstone project that allows you to investigate a substantial real-world problem within an area relevant to your programme of study. Drawing on the knowledge and skills developed throughout your degree, you will design, plan, and execute an in-depth investigation, either individually or as part of an approved team project with clearly defined individual responsibilities. You will apply appropriate research methods, analytical techniques, and critical evaluation skills to address complex challenges and communicate evidence-based findings. The module encourages consideration of sustainability, equality, diversity, inclusion, and global perspectives throughout the project lifecycle. Supported by a dedicated supervisor, you will also engage with professional, ethical, and intellectual property considerations while developing advanced problem-solving, project management, and communication skills that demonstrate readiness for professional practice.