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 Top reasons to choose this course

• Explore advanced topics in AI such as logic, natural language processing (NLP), and speech and image recognition, preparing you for emerging applications of artificial intelligence and data science
• Master core industry-standard tools including SQL, SAS and R statistical software, widely used in medical and pharmaceutical companies, banking and government, alongside Python for programming, software development and machine learning.
• Work towards an additional globally recognised SAS qualification alongside your degree, adding further value to your CV and enhancing your career prospects.
• Work with real data across topics such as social justice, sustainability and public health, applying your learning to contemporary challenges and real-world data sets.
• Collaborate on projects with organisations including Brighton and Sussex Medical School (BSMS), Brighton & Hove Buses, Southern Water and EDF Energy, gaining valuable insight into industry practice.
• Boost your CV with an optional paid placement year and industry-linked projects embedded throughout the course, helping you build professional experience and industry connections before graduation.
• Develop career-ready skills through teamwork, communication, project work and learning materials created in collaboration with leading experts in statistics and analytics from the SAS Institute.
• Take advantage of Brighton’s thriving digital economy, with opportunities to network with industry groups including Silicon Brighton and Brighton R, attend industry events, and gain exposure to the region’s vibrant tech community.
• Learn in specialist facilities equipped with professional software including SAS, R, SQL and Python, supporting hands-on learning throughout your studies.
• Contribute to research and innovation through your final-year Data Science Project, with opportunities to collaborate with industry partners and work alongside academic researchers

Year 1 

In your first year you will be introduced to statistics, programming, visualisation, databases and core mathematics so you’ll gain all the mathematical and statistical background you’ll need for your studies.

In your first year your classes will be over three days each week and they will not start before 10am. You may have other activities on campus on other days.

Modules

• Operations and Information Systems

  This module introduces you to digital technologies and operations management in business. You’ll explore contemporary issues, innovation and future trends in IS and operations management, examining how managers use digital technologies to measure and improve operations and systems performance. You’ll also develop an understanding of processes and how to improve them in both service-oriented and manufacturing-sector organisations. 

• Introduction to Data Analysis with SAS

  You will get an introduction to SAS programming and the basic statistical tools needed to use code to manipulate data and use the ‘point and click’ interface available in SAS Studio to perform data analysis. The module includes teaching materials developed in collaboration with industry-leading experts in statistics and analytics from the SAS Institute. You’ll gain the skills to manage and join multiple data sets, transform data, explore and visualise data.

• Computing Technologies

  This module will give you an understanding of the basic principles underlying the architecture and operation of a computer system, including the storage and retrieval of data. Content covers computer organisation and architecture; computer arithmetic, binary and hexadecimal number systems; system software; software tooling; data storage and management; relational database implementation; and an introduction to Structured Query Language (SQL).

• Fundamentals of Programming for Data Science

  This module will introduce you to foundational programming using Python and R. It has a data science focus, enabling you to perform data science tasks such as data manipulation, visualisation and basic analysis. You will use Excel spreadsheets as a tool for organising and displaying data vs DataFrames in Python and R.

• Essential Mathematics for Data Science

  You will learn the fundamental mathematical knowledge and skills required to successfully study for degree programmes in data and computing sciences. It will cover some of the basic concepts in algebra, geometry and calculus, and you will develop the ability to solve problems using the mathematical concepts introduced during the module.

• Introduction to Statistics

  This module introduces you to statistical and probability concepts and their real-world applications. Software packages such as SPSS and Microsoft Excel will be used to provide you with a grounding in industry appropriate statistical software. You will gain the skills required to apply statistics to real data sets together with the appropriate theory behind the applications.

Making sure that what you learn with us is relevant, up to date and what employers are looking for is our priority, so courses are reviewed and enhanced on an ongoing basis. When you have applied to us, you’ll be told about any new developments through .

Year 2 

In year 2 you will cover more advanced data analysis methodologies and strengthen your mathematical and programming skills. You will also develop your communication skills and your awareness of ethical issues related to managing data in a business environment.

The university teaching hours are between 9am and 6pm. In year 2 the days and times you will need to be on campus will depend on a number of things including the type of teaching on your course, and time spent in the library and on research.

Modules

• Linear Algebra and Further Calculus

  This module will introduce you to the fundamental concepts of linear algebra – such as vector spaces, subspaces, linear independence, linear transformations and further matrix algebra – and of multivariable calculus. Examples will enable you to develop an understanding of the general common principles that underlie many real-world applications.

• Database Management Systems

  This module explores the relational database model. You will gain practical experience in building database systems using this technology and explore the functions and features of database management systems and their role as part of an effective information system. 

• Programming for Data Science

  Programming for Data Science enables you to build on skills developed in year 1 and further explore concepts in programming, such as the object orientation approach, with a focus on how these can be applied to data science. You will use programming languages such as Python and R.

• Applied Machine Learning

  This module introduces the key concepts and ideas of machine learning in the context of practical data analysis, enabling you to use machine learning methods to extract value and insight from data. You will explore the common types of learning tasks, how to solve them and how to evaluate the quality of solutions. 

• Applied Statistical Modelling

  This module is covers a range of data analysis techniques including linear regression, logistic regression, poisson regression, multinomial logistic regression and ordinal logistic regression. The emphasis will be on the practical application of these methods using statistical software such as R, SAS and Python and real-world data sets. 

• Data Storytelling and Visual Analytics

  The availability of  ‘big data’ has provided the means to collect vast amount of information which requires specialist skills to effectively disseminate to diverse audience. In this module, you will learn modern data visualisation tools and techniques to generate and communicate insights from data. You’ll also work on your report writing and presentation skills, focusing on best practice. 

Placement year

You have the option of a year’s paid placement after your second year. Going on a placement builds confidence, can help you decide what you want to specialise in during your final year and strengthens your CV.

Our students have been on placement with companies including:

• Disney
• IBM
• Lloyds
• Pfizer
• The Pensions Regulator
• British Airways
• Gatwick Airport
• AstraZeneca.

Learn more about placement opportunities at Brighton.

Final year

You choose five modules from a range of options. The Data Science Project is compulsory and provides opportunities to present your final year project at our Computing Degree Show, showcasing to industry professionals, employers and the wider public.

The show provides a platform to demonstrate your technical skills, creativity and innovation, while building valuable connections that can lead to graduate roles or further opportunities.

The university teaching hours are between 9am and 6pm. In your final year the days and times you will need to be on campus will depend on a number of things including the option modules that you choose.

Modules

Core modules

• Data Science Project

  On this core module you will research a data science problem or topic in depth and present the results of your research to a small panel of lecturers. This is your opportunity to demonstrate your ability to explore new material, apply the skills, knowledge and techniques that you’ve learned during your studies and present your findings in a formal context. 

Options*

• Advanced Artificial Intelligence

  On this module you will examine current development in AI techniques and common issues that arise in real-world applications. You’ll develop an understanding of, and the skills needed to explore, advanced topics in AI such as logic, natural language processing (NLP) and speech and image recognition.

• Data Mining

  This module will help develop your understanding of data mining concepts and how they can be applied to solve real-world complex problems. You will explore areas including text mining, information extraction and information indexing, with specific algorithms and techniques for feature extraction.

• Time Series and Forecasting Models

  This module enables you to explore time series analysis used to model non-seasonal and seasonal data, and to analyse trends, patterns and seasonal variations over a time period. You'll be introduced to the concepts and methods used to predict future observations in different contexts such as to analyse financial data, stock prices, temperature readings, predict sales or marketing revenue. 

• Advanced Statistical Modelling

  On this module you will explore how to apply recent developments in statistical modelling, Bayesian approaches and complex statistical models to real-world problems using statistical software such as R, SAS and Python. An introduction to theory will help develop your understanding and you will examine real case studies so you can apply the theory and overcome the challenges of real data. 

• Natural Language Understanding

  This module enables you to develop the knowledge and skills relevant to natural language understanding (NLU) which is used in industry. You will explore NLU state-of-the-art techniques and apply artificial intelligence approaches to solve real-world problems and improve business performance. 

• Medical Statistics for Data Science

  This module focuses on the application of statistical concepts, models and techniques to analyse, compare and evaluate data from medical studies. You will explore the importance of statistics in medicine and develop an awareness of the problems associated with clinical medicine.

*Option modules are indicative and may change, depending on timetabling and staff availability.

Discover how Computer Science with AI student Roland Matthews applied artificial intelligence to a real-world healthcare challenge in his final-year project.

Facilities for learning 

You'll benefit from access to a wide range of specialist facilities designed to support hands‑on, practical learning.

• Usability lab – equipped with advanced eye‑tracking technology to analyse how users interact with digital systems and improve user experience design.
• Security labs – including CISCO‑accredited cyber suites for developing secure and dependable software systems and understanding network protection.
• VR and digitisation lab – featuring Oculus Rift and HTC Vive headsets.
• 3D scanning and printing technologies – for immersive and interactive development.
• Reflectance Transformation Imaging (RTI) acquisition dome –supporting research and projects in digital imaging, visualisation and analysis.
• Simulation Suite – home to flight and car simulators using VR and motion platform technology, supporting real‑world modelling and testing.
• Specialist computing labs – dual‑boot Windows and Linux PCs with access to industry‑standard tools such as IntelliJ, Eclipse and Visual Studio Code, plus database systems including Microsoft SQL Server and MySQL.

Across these facilities, you’ll work with programming languages including Python, JavaScript, Java and C++ gaining practical experience with the tools, technologies and environments used in industry.

Staff profile

, principal lecturer

“Central to my approach as course leader is the emphasis on collaboration and interdisciplinary learning. I believe that data science is inherently interdisciplinary, drawing from fields such as statistics, machine learning, computer science and domain-specific knowledge. Therefore, I strive to create opportunities for students to work collaboratively on projects that mirror real-world scenarios where they can leverage their diverse backgrounds and expertise to tackle complex challenges.

“Through teamwork, peer-to-peer learning and engagement with industry partners, students gain invaluable teamworking and communication skills that are essential for success in today’s data-driven world.

“In addition to teaching, my research focuses on developing innovative statistical methods, which I distribute as open-source software (R packages) and apply in interdisciplinary projects.

“As a Fellow of the Royal Statistical Society and Treasurer of the British and Irish Region of the International Biometric Society, I am heavily involved in professional development activities, participating in conferences, workshops and collaborations within the data science community.”

Course fees

This course offers a professional placement (sandwich) year in year 3. If you choose to take this placement you will pay a reduced fee during this year. For students starting their undergraduate degree in the 2026–27 academic year, the fee for the professional placement year is £1,958. This fee may be subject to small increases in line with inflation.

The course fees shown here are what you will pay for the first year of your course if you start in the academic year 2026–27 and you are studying full-time (fee amount is subject to parliamentary approval). You pay fees in each year of your course. The fees you pay may increase each year.

Regulated fees

Fees for UK students on undergraduate and some postgraduate courses are set by the UK government. These are known as regulated fees. From academic year 2026–27, regulated fees will increase annually in line with forecast inflation.

Student Finance England loans for tuition fees and maintenance will also rise in line with inflation.

If you are studying part-time your fee will usually be calculated based on the number of modules that you take.

Unregulated fees

Course fees for international students taking undergraduate courses and for most postgraduate courses are not regulated by the UK government. Unregulated fees may increase each year by up to 5% or the forecast Consumer Price Index (CPI) inflation (whichever is higher).

Find out more

• Fees, bursaries, scholarships and government funding info for UK and international undergraduate students
• Student finance and budgeting while studying
• Read our student contract and (pdf) for more on University of Brighton tuition fees.
• Brighton Boost – cost of living help for our new undergrad students. Find out about how we can help with your study, accommodation or travel costs and more.

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