BSc (Hons) Data Science (Top up Programme)

This module refreshes your knowledge of relational databases and SQL, exploring queries of greater complexity and diving deeper into the theory and practice of database design, performance tuning, security and recovery. In the second half of the module, you will consider the practical limitations of relational databases, and you will be introduced to some of the NoSQL alternatives, such as document databases and graph databases.

This module explores a range of advanced topics in machine learning, broadening your knowledge and preparing you for real-world challenges. You will examine algorithms for anomaly detection, dimensionality reduction, and semi-supervised learning, alongside techniques such as AutoML for model selection and optimisation. The module also addresses responsible AI, equipping you with skills to apply explainability techniques, detect and mitigate algorithmic bias, and ensure ethical machine learning practices. Additionally, you will consider the complexities of real-world deployment, including MLOps, monitoring models in production to address issues like concept drift, and leveraging cloud platforms for scalability and efficiency.

In this module, you will deepen their understanding of statistical concepts and apply them to real-world scenarios. You will explore how statistical methods are used to inform decision-making in business, focusing on practical applications such as confidence intervals for analysing survey data and assessing the reliability of estimates, hypothesis testing in A/B split testing for optimising marketing strategies, and time series analysis to model and forecast future trends in sales, demand, or market performance. 

This module will extend your knowledge and understanding in two ways. Firstly, it will review the content covered at Level 5 at a deeper level. Secondly, it will broaden students' understanding of a range of practical applications of deep learning, including the use of large language models (LLMs) in natural language processing (NLP), foundation models in computer vision, and techniques such as reinforcement learning and generative models.

Students will work on a self-directed Data Science project with supervision, developing their skills in researching and implementing a solution to a Data Science problem which presents significant challenges.