Course overview
Start from the fundamentals and progress into modern data science.
Begin with a foundation year in mathematics, programming, cyber security, robotics and the Internet of Things, then develop practical expertise in data analysis, engineering, statistics, artificial intelligence and machine learning.
Foundation Year
Build core knowledge in cyber security, robotics and the Internet of Things, mathematics and programming before beginning degree-level data science.
Year One
Develop programming, data-analysis, information-systems, financial-mathematics and logical problem-solving foundations.
Year Two
Study data engineering, analytics, databases, statistical modelling, responsible professional practice and smart data discovery.
Year Three
Apply artificial intelligence, machine learning, big data, web development and project methods in a substantial final project.
Blended learning
Combine online and on-campus study through daytime or evening/weekend timetables, with practical learning supported by tutorials, workshops and computer-lab activity.
Curriculum
A computing foundation year followed by three years of data science.
01Foundation Year
Level 3
120 credits
Foundation Year
Level 3
- Cyber Security Fundamentals30 credits
- Introduction to Robotics and Internet of Things30 credits
- Mathematics30 credits
- Programming30 credits
01Year 1
Level 4
120 credits
Year 1
Level 4
- Data Analysis15 credits
- Financial Mathematics15 credits
- Fundamentals of Computing15 credits
- Introduction to Information Systems15 credits
- Logic and Mathematical Techniques30 credits
- Programming30 credits
02Year 2
Level 5
120 credits
Year 2
Level 5
- Data Engineering15 credits
- Programming with Data15 credits
- Data Analytics15 credits
- Databases15 credits
- Professional and Ethical Issues15 credits
- Smart Data Discovery15 credits
- Statistical Methods and Modelling Markets30 credits
03Year 3
Level 6
120 credits
Year 3
Level 6
- Artificial Intelligence and Machine Learning15 credits
- Career Development Learning15 credits
- Data and Web Development30 credits
- Artificial Intelligence and Big Data in Business15 credits
- Project Analysis and Practice15 credits
- Project30 credits
Module availability and content may change as part of the provider’s ongoing academic review.
Assessment
Practical ways to show what you know.
Teaching uses lectures, tutorials, workshops and practical computer-laboratory sessions.
Assessment includes coursework, in-class tests, quizzes, multiple-choice tests and unseen examinations.
Programming, analytics and project modules develop the ability to apply technical and mathematical ideas to practical data problems.
Entry requirements
Your route onto the course.
Academic requirements
- 32 UCAS points.
- GCSE Mathematics grade 4/C or an accepted equivalent.
- Applicants with relevant experience who do not meet the standard requirements may be considered individually and invited to an academic interview.
English language
- GCSE English grade 4/C or an accepted equivalent.
- IELTS 5.5 with no component below 5.5, or an accepted equivalent.
Interview
Applicants may be invited to an admissions interview about their motivation or an academic interview to assess subject readiness where standard requirements are not met or they have been out of education for some time.
Application documents
- Passport personal-details page.
- Qualification certificates and transcripts, with certified translations where required.
- A personal statement of more than 250 words where requested.
- A CV where requested.
Student visa sponsorship
The university centres currently state that they cannot sponsor international students for this programme. Applicants requiring Student visa sponsorship should confirm alternative eligible programmes directly with the university before applying.
Fees & funding
Understand the full cost.
Annual fees may increase in line with inflation and government guidance.
Tuition covers teaching, access to resources, registration and student support services. It does not include optional books, stationery, printing, accommodation, living costs, travel or leisure activities.
The course information recommends allowing approximately £200 per year for optional course texts.
Access to a laptop or desktop computer with a microphone, speakers, webcam and reliable internet connection is required for live online sessions and assignments.
Funding your study
Eligible UK students may be able to receive government Student Finance support towards tuition and living costs.
Eligibility depends on personal circumstances, previous study, the course and the provider.
What you’ll achieve
Skills for ideas that move forward.
Develop programming, statistical, mathematical and data-engineering skills from foundation level through to honours-degree study.
Use artificial intelligence, machine learning, databases, analytics and responsible professional practice to solve data-driven problems.
Career opportunities
Where this course can take you.
Career outcomes depend on your experience, further training and the requirements of each role.
Data analysis
- Data analyst
- Associate data analyst
- Business intelligence analyst
Data science
- Data scientist
- Data science operations officer
- Junior machine-learning specialist
Data engineering
- Data engineer
- Database developer
- Analytics engineering support
Digital projects
- Data project assistant
- Technical analyst
- Machine-learning project support
Frequently asked questions
Course questions.
What will I study in the foundation year?
The foundation year covers cyber security, robotics and the Internet of Things, mathematics and programming before the degree curriculum begins.
Do I need my own computer?
Yes. The published course information requires access to a suitable laptop or desktop computer, webcam, microphone, speakers and reliable internet connection.
How is the course assessed?
Assessment combines coursework and practical activity with in-class tests, quizzes, multiple-choice tests and unseen examinations.