Data Science (Distance Learning) BSc(Hons)

This module provides you with a comprehensive introduction to the mathematical foundations of probability and statistical methodology, essential for understanding and analysing data in a range of real-world contexts. You will learn how to apply appropriate statistical techniques to solve problems, interpret results and draw meaningful conclusions. Core topics include probability spaces, conditional probabilities, Bayes theorem, discrete and continuous random variables, statistical distributions, independence, density and mass functions, variance, standard deviation and expectation. You’ll also gain experience in statistical sampling and sampling distributions and explore key sampling distributions, including chi squared and t-tests. By combining theoretical understanding with practical application, this module equips you with the critical skills needed for statistical modelling and data interpretation—an essential toolkit for modern data science.

This module introduces fundamental concepts in linear algebra – a key area of mathematics for understanding data structures, algorithms and computational methods. This module will provide you with a thorough grounding in matrix theory, including properties of matrices (determinant, rank, inverse etc.) and their use in solving systems of linear equations. Topics include existence, ill-conditioning, linear dependence, orthogonality, QR factorisation, Cholesky factorisation, LU factorisation and other solution methods. You will be introduced to the concepts of eigenvalues and eigenvectors, along with methods for determining eigen-solutions using both deterministic and numerical methods.

In this module, we introduce you to basic computing science and mathematical concepts related to software development. You will explore topics such as set theory, graphs and trees, finite state machines, grammars and languages, propositional logic and searching and sorting algorithms. You’ll put the theory into practice, gaining hands-on experience using a programming language and software that lets you directly implement finite state machines.

This module aims to introduce you to fundamental programming concepts, using a programming language that is widely adopted for data analytic workflows. In the first part of this module, you will gain knowledge in core programming elements and constructs, followed by an introduction to programming packages and modules that are designed for basic data processing and analytic tasks.

This module provides you with the opportunity to investigate multivariate systems in depth, essential for understanding complex, real-world data. You’ll develop the skills and experience to formulate and apply statistical models and execute techniques associated with multivariate analysis on data sets, preparing you to tackle data challenges commonly faced in professional environments.

This module aims to equip you with the knowledge and skills needed to design, implement and query a relational database. You’ll be supported in gaining an understanding of the functionality necessary to enable web pages to interact with a database. You’ll be given the opportunity to become familiar with web architectures and the design considerations necessary for implementing a database driven web application.

What does it mean for a machine to be intelligent, and how close are we to achieving it? In this module, you will delve into the intriguing question of whether machines, particularly computers, can truly be intelligent—and if so, what does that means in practical terms? In the first half, you’ll focus on environments where everything is predictable, like laboratory conditions or puzzle-solving scenarios exploring how machines represent knowledge, solve problems, and plan actions under these ideal settings. The second half introduces uncertainty, teaching you how machines adapt to opponents, incomplete information, and unpredictable situations. You’ll also discover how intelligent agents learn and make decisions. By the end, you'll gain a deeper understanding of how machines perform tasks that seem intelligent across different contexts.

Building on your foundational knowledge of statistics, this module introduces a range of advanced statistical techniques, which can be used to help solve real-life problems involving the analysis of data and interpretation of results. You will explore sampling theory and estimation of mean and variance, hypothesis testing, significance testing, measures of independence and goodness of fit. You will also study linear, multiple and stepwise regression, correlation and analysis of variance tests. Throughout the module, you’ll apply these techniques to practical datasets, using powerful tools such as R and Minitab to carry out analysis and interpret results.

In this module, we’ll support you in enhancing your programming skills to cover a range of standard data structures (such as lists, trees and graphs) and algorithms (including searching, sorting and traversals) for both sequential and concurrent systems. You will also study how to analyse systems to determine their complexity, correctness and safety, and to calculate their efficiency.

Recent advances in information technology have enabled the extensive collection of textual data across various domains, driving significant developments in text analytics and natural language processing (NLP) approaches. During this module, you’ll gain a fundamental understanding of these approaches, particularly advanced techniques for processing and analysing textual data to extract meaningful insights. We will explore several widely known methods, including text classification, clustering, and sentiment analysis, and investigate typical applications and potential limitations. We will guide you in selecting the right techniques for different text analysis challenges, weighing the strengths and weaknesses of each approach. You will have the opportunity to apply this knowledge using industry-standard tools and delve into high-profile applications such as conversational agents and information retrieval systems.

The exponential growth of digital data is transforming how organisations manage and utilise information. In this module, you will explore why traditional databases are no longer sufficient to meet modern demands and discover alternative data modelling techniques. You’ll dive into approaches including hierarchical, network, object-oriented and object-relational models, gaining a clear understanding of their applications and benefits. By the end of the module, you’ll be equipped with the knowledge to tackle complex data challenges in an increasingly data-driven world.

In this module, you will explore how data science has revolutionised the way organisations harness data to achieve business success. You’ll examine how data analytics can shape business strategies, uncover opportunities for innovation, and deliver a competitive edge. The module highlights the importance of planning, executing and managing data science projects using agile methods and project management tools. You’ll also develop the skills to lead and collaborate with cross-functional teams, ensuring clear communication between data scientists and stakeholders. By applying analytical skills, you’ll learn to interpret data insights and make strategic management decisions to boost organisational performance.

This module offers you the opportunity to study and investigate a specific mathematical topic in depth, providing you a hands-on experience in carrying out a mathematical project, mirroring the process that would take place in a professional environment. You will be allocated a project supervisor who will direct you through the process of developing project objectives, project planning, performing background research, and carrying out the project work to a satisfactory conclusion. The project will focus on a data science challenge relevant to your chosen career pathway. The project work aims to extend your knowledge and capabilities in the specific field associated with the project, and allow you to demonstrate your initiative, commitment, and mathematical capability to a professional level.