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With the UK aiming to become a global leader in AI, now is the perfect time to boost your degree with the latest skills in data analytics and artificial intelligence. This is an additional year that you can integrate into your current degree. You don’t need a background in maths or computer science, just a grade 5 or above in GCSE Maths.
The course is designed to ensure you first gain a solid grounding in the foundations and then build on this knowledge throughout the year. Taught by the Schools of Mathematical Sciences, Computer Science, and the Business School, you’ll learn from a spectrum of technical, computing, and professional expertise.
Why do this additional year?
Practical-focusNo formal exams; you’ll be assessed via portfolio-building projects |
Skill-buildingModules designed for hands-on learning in real-world contexts |
Career-readyCreate a portfolio to showcase your skills to employers |
As more and more roles require an understanding of data, and as AI becomes a larger part of day-to-day work, organisations have a growing expectation that graduates can work with and comprehend data concepts. As such the Year in Data Analytics and AI course meets a very real need in the market.
Dan Kellett
Director of Lending and Analytics, Carmoola
Programme overview
Throughout this one-year course you'll build a solid foundation in computing, mathematics, and statistics to explore, model, and interpret data. In the autumn semester, you'll dive into data analytics and AI principles and applications, while also developing teamwork skills through real-world group projects.
As you move into the spring semester, you'll deepen your understanding of modern data analytics and AI techniques and learn specialised methods. You'll also start implementing machine learning techniques using Python, further enhancing your ability to tackle complex data challenges.Ìý
Modules
Develop foundational skills in programming and mathematics. You will learn how toÌýwrite functions in PythonÌýandÌýuse databases in SQLÌýto store and retrieve data. This module lays the essential foundations of mathematics to build more advanced techniques inÌýdata analytics and AI later in the course.
LearnÌýhow toÌýcommunicate initial insights about data through exploratory data analysis. You will be taught how toÌýuse statistical techniques to model and interpret data. You will understandÌýdata collectionÌýand how it affects the quality of the data. This module will enable you toÌýpresent your findingsÌýthrough different mediums, including reports and presentations.
GainÌýspecialised analytical skillsÌýto tackle real-world analytics projects across diverse domains. You will learn toÌýselect and apply appropriate analytical techniques,Ìýreflect on data limitations and biases, andÌýuse a range of toolsÌýfor end-to-end analytics.
By the end, you will be able toÌýcommunicate findings effectivelyÌýto various stakeholders, bringing competitive advantages to organisations.
This module introduces you to Machine Learning (ML) techniques and their implementation in Python. You will learn key ML concepts and explore different types of ML models. You will also get an overview of various ML methods, from classical techniques like decision trees to modern ones like neural networks.
By the end, you will be able to apply ML techniques to solve data problems using Python and develop essential ML skills for the modern workplace.
A hands-on introduction to Artificial Intelligence (AI). In the first semester, you'll build foundational AI and digital Literacy, mastering what AI is, its ethical implications, and the crucial connection between AI and Machine Learning. In the second, you'll explore modern generative AI techniques to expertly create sophisticated content, such as text and images, and develop AI skills for professional productivity.
You will apply previously learnt techniques to open-ended problems typical of your subject area. You’ll developÌýskills highly sought by employers, including understanding work-related demands and real-world problems, flexibility, proficient oral and written communication, and teamwork.
