BSc (Hons) Data Science with foundation year Bachelor's degree at Coventry University

This module introduces you to fundamental algebraic concepts (sets, functions, basic group theory, linear systems, introductory eigenvalue/eigenvector theory and vector spaces). It should help you develop your computational skills in solving and interpreting linear equations. It aims to provide you with the essential building blocks in algebraic thinking, forming a foundation for more advanced topics in subsequent years.

This module introduces you to key statistics and data science techniques. In this module you will consider data summaries, exploratory data analysis, data visualisation, probability concepts, estimation and hypothesis testing. Explore the predictive analytics methods of regression and correlation and gain hands-on experience using modern software tools to analyse real datasets. This module includes content on probability and statistics, data wrangling and data visualisation and the practical app

This module introduces you to single-variable calculus, covering limits, continuity, derivatives and integrals which should create a solid base for later study. Learn about ordinary differential equations, sequences and series. This module includes topics such as foundations of calculus and sequences, differential calculus, integral calculus, complex numbers, an introduction to ordinary differential equations and power series. It also focuses on real-world uses such as optimisation and rates of

This module introduces you to programming and artificial intelligence. Learn the fundamentals of programming, how to write clear and effective code, and develop your problem-solving and algorithmic thinking skills, with a focus on AI applications. The module focuses on simple AI programming challenges, including games, chatbots and maze solvers, along with introductory material on machine learning, neural networks and generative artificial intelligence. Content includes necessary mathematics, as

Extend your understanding of single-variable calculus into multivariable domains, exploring partial differentiation and multiple integrals. You will be introduced to vector calculus concepts (for example, gradient, divergence, curl) and apply these to geometric/physical problems. Develop knowledge of complex analysis, focusing on analytic functions, contour integrals and basic residue theory. These advanced calculus methods will be linked to real-world or theoretical scenarios to reinforce your

Learn the fundamental concepts, models and applications of multiple regression, analysis of variance (ANOVA) and machine learning. These techniques will be explored and applied to a variety of real datasets. Hypothesis testing and estimation will be extended within a statistical data modelling framework. The methods taught in this module are extensively used in industry, commerce, government, research and development. Module content will address wider social, ethical and professional issues of a

Examine broader solution techniques (transform methods, series methods) for both linear and selected nonlinear equations. You will be introduced to Fourier-type expansions and Sturm–Liouville theory for solving boundary-value problems in ordinary differential equations. Study systems of ordinary differential equations (ODEs), both linear and nonlinear, with a focus on stability and qualitative behaviour. This includes working with simple classifications and elementary solution strategies for par

Explore the practical applications of artificial intelligence (AI) across various industries. Get hands-on experience with AI tools and techniques, understand the ethical implications of AI and develop skills to implement AI solutions in real-world scenarios. Covering key AI concepts and a broad overview of its applications, this module aims to teach machine learning, exploring supervised and unsupervised learning, neural networks and deep learning, with practical exercises to reinforce these co

This module provides you with an opportunity to reflect upon and gain experience for an approved placement undertaken during your programme. A placement should usually be at least 26 weeks or equivalent; however, each placement will be considered on its own merits, having regard to the ability to achieve the learning outcomes.

This module provides you with an opportunity to reflect upon and gain experience for an approved international study/work placement undertaken during your programme. A work/study placement should usually be at least 26 weeks or equivalent; however, each placement will be considered on its own merits, having regard to the ability to achieve the learning outcomes.

Get an understanding of optimisation techniques and how they are used in artificial intelligence (AI) and machine learning (ML). Explore the maths behind solving different types of optimisation problems, both with and without constraints and with discrete or continuous variables. This module will explore a variety of optimisation techniques in AI and ML, including gradient descent, batch gradient descent, stochastic gradient descent, Mini-batch gradient descent and Adam. You will investigate adv

This module introduces you to advanced topics in statistics and data science. Using relevant software, learn how to critically examine data and choose and apply appropriate analyses using a range of methods. The material will be set within a range of different contexts taken from industry, medicine, agriculture and engineering. This module will be particularly useful if you are interested in postgraduate study in statistics, or a career in statistics or data science.

The individual project module provides you with an opportunity to apply and integrate knowledge and skills acquired throughout your degree programme in a substantial individual piece of work. The module aims to develop your ability to solve real-world problems, engage in independent research, enhance your professional skills and demonstrate technical proficiency in your chosen subject area. This module should help you develop your project planning and time management skills. It will also test yo

Deep learning is a part of machine learning that uses layered neural networks to understand complex patterns in data. Learn the fundamentals of neural networks and deep learning and build practical skills in designing, testing and evaluating neural network models. Explore more advanced deep learning topics and techniques, such as convolutional neural networks, recurrent neural networks and generative models. This module also looks at how deep learning is used in real-world applications including

This module integrates higher-level algebraic and number-theoretic concepts (modular arithmetic, finite fields) into the study of cryptography and basic information theory. Learn how abstract algebra underpins classical and modern cryptographic protocols, as well as the fundamentals of encoding/decoding information.

Review the fundamental concepts of probability and statistics and develops deeper understanding of statistical models, estimation, inference and stochastic processes. Survey a range of statistical and machine learning models (including supervised learning and unsupervised learning) with applications to real-world datasets. Consider techniques for model evaluation and discuss wider modelling issues such as bias, fairness, under/overfitting, interpretability and scalability. Build a solid foundati

Develop fundamental concepts, algorithms and architectures of artificial neural networks, deep learning and image processing. These fundamentals are applied and integrated in a broad range of real-world applications with a particular focus on practical and computational challenges in computer vision. This module will provide an in-depth exploration of the latest advancements in deep learning and computer vision. It aims to offer practical experience with real-world applications, enabling you to

Build knowledge and understanding of the concepts, algorithms, limitations and challenges of such artificial intelligence technologies, alongside developing practical skills implementing and experimenting with these systems. This module covers advancements in generative AI and reinforcement learning, equipping students with knowledge of technologies that are revolutionising various industries. It emphasises hands-on projects where you can implement generative models and reinforcement learning al