BSc (Hons) Data Science

Harness the future of tech with our data science degree. Develop expertise in data analysis, machine learning, and language processing. Gain experience solving real-world problems and prepare for a career in the rapidly growing field of data science.

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Typical offer: 120–128
Entry requirements
Fees: £9,535 per year (UK)
Full details
UCAS code: G200
Course length: Full time: three years
Find out more
Location:
University of Hertfordshire, Hatfield
Institute code: H36

93% of Herts graduates are in work or further study 15 months after graduation (Graduate Outcomes, 2021/22)

Brand new School of Physics, Engineering and Computer Science building

Benefit from strong links to business and research

Course overview

The BSc (Hons) Data Science degree at the University Hertfordshire is designed to equip students with the skills to thrive in the fast-growing field of data-driven decision-making. Combining programming, machine learning, and big data technologies, this course emphasises hands-on experience with real-world applications. Through industry-aligned modules and cutting-edge tools in our new state-of-the-art home of computer science, you'll learn to extract valuable insights from complex datasets. Graduates are prepared for roles in data analysis, business intelligence, and AI development, with strong career prospects in sectors where data is reshaping industries.

Why choose this course?

Data drives the modern world, shaping industries and everyday life, from healthcare to global finance. Our data science degree equips you with advanced skills in programming, IT, and machine learning, preparing you for a dynamic career in the tech industry.

State-of-the-art facilities: On this degree you will access Spectra, our new home for the School of Physics, Engineering, and Computer Science, fully equipped with industry-standard software, hardware, and simulators for an immersive tech experience.

Work placement opportunities: Our dedicated Careers and Employment team will guide you through securing the right placement to gain industry experience. Recent students have held placements with major organisations such as Apple, BT, IBM, Intel, GSK, Microsoft, and Xerox, while others have chosen smaller, family-run companies closer to home. These experiences provide invaluable hands-on learning and industry connections.

Strong employment prospects: 93% of Herts graduates are in work or further study 15 months after graduation (Graduate Outcomes, 2021/22).

Your future career

BSc (Hons) Data Science graduates from Herts are well-prepared for careers in the tech industry, equipped with highly sought-after programming, data analysis, and visualisation skills. An impressive 93% of Herts graduates are in employment or further study 15 months after graduation (Graduate Outcomes, 2021/22).

Typical areas of work for data science graduates include:

data analyst
business analyst
machine learning engineer
data scientist
mobile app and web developer
statistician

After completing the data science degree, you’ll hold a versatile skill set that leads to career opportunities spanning various sectors. You will also benefit from CV enhancing opportunities such as employer competitions, insight days, digital programmes, volunteering, student leadership roles, and achievement awards.

See what recent graduates are up to

What you'll study

In this course, you'll gain hands-on experience with industry-standard tools and technologies to build a solid foundation in data science. You'll learn core principles in programming, data modelling, and mathematics, while applying this knowledge to real-world projects. By the end of your studies, you will be equipped with the technical skills to develop innovative software solutions and tackle complex challenges across diverse sectors.

In your first year you will learn the foundations of the subject including a development of your mathematical understanding, IT literacy and your awareness of data presentation principles.

During your second year you will deepen your understanding and develop more advanced skills. There are technical modules on machine learning, information theory and financial markets. This course applies real-world principles, so you will also dive into cryptocurrencies and, if you choose, investment portfolios.

For your final year you will study complex linear modelling topics, multivariate statistics and take a look into next generation data science. Finally, you will undertake an individual project.

Level 4

Module	Credits	Compulsory/optional
Mathematical Methods	15 Credits	Compulsory
On entry to the degree students have a range of mathematical backgrounds and knowledge, and this module has been designed to standardise mathematical knowledge through the first semester of study. You will review core areas of A-level mathematics, which will be extended to enhance your knowledge. You will learn important and widely applicable mathematical techniques relating to hyperbolic functions, matrices, vectors, differential and integral calculus, and complex numbers. In this module you will develop your skills in mathematical problem solving, analysis and evaluation.
Probability and Statistics	15 Credits	Compulsory
This is an introductory module in statistics and probability theory. Basic ideas such as initial data analysis, the mean, standard deviation and variance, probability, statistical distributions, the use of a statistical package, the use of data for inference and decision making are covered. This involves confidence intervals for means and techniques for hypothesis testing for differences between means, differences between proportions and for the size of a correlation. Other topics covered include simple linear regression, and the analysis of category data and contingency tables.
Linear Algebra	15 Credits	Compulsory
Linear algebra is a fundamental area of mathematics and is arguably the most powerful mathematical tool ever developed. It is a core topic of study with applications to fields as diverse as business, economics, engineering, science etc. In this module you will learn basic concepts of linear algebra, including matrices, eigenvalues and eigenvectors, that provide the first step towards the understanding of how to use these tools in pure and applied mathematics.
Computational Modelling	15 Credits	Compulsory
In this module you will learn how to use a programming language. You will solve exercises during practical classes and independent study time. You will see how to develop scientific and mathematical models and how they can be implemented in a computational environment. The module develops skills in problem solving, computer programming, analysis and evaluation, and interpersonal and communication skills
Principles of Data Science	15 Credits	Compulsory
The module is designed to provide students with the principles of planning, conducting, and critically reviewing data scientific research in applied contexts. By the end of the module, students will be confident with the language and conventions of data science, including types of data, data structures, and the necessary steps to perform a data science project, including the ethical and legal aspects.
IT Literacy and Core Skills	15 Credits	Compulsory
The module is designed to provide students with basic literacy in the use of IT and confidence in interacting with computers running different operating systems. It will develop a broad understanding of how computers store and communicate data, and how computers can be programmed in different ways. It will also develop skills in using the LaTeX document preparation system and understanding of the principles by which computers are programmed. By the end of the module students will be able to navigate different operating systems, interact with file systems using the command line, and be able to produce professional-quality typeset documents using LaTeX.
Data Science Laboratory 1	15 Credits	Compulsory
This module is laboratory based, providing students with hands-on training in several techniques fundamental to data science. It also provides an environment where students have an opportunity to hone general coding skills, including good practice in software development. All students will create and maintain a git repository for their code and learn the principles of proper version control of software. Through a series of self-contained data science 'challenges' students will learn and put into practice; the key principles of data engineering, such as dealing with noisy and missing data; design and implement algorithms and data analysis pipelines to tackle a variety of problems in the most efficient manner; and critically analyse outputs and communicate findings to others.
Data Science Small Group Tutorial 1	0 Credits	Compulsory
The module will require students to attempt a range of problems, mostly of a data science nature, in the students' broad subject area. Some problems will be associated with other specific taught modules on the programme, while others will have a synoptic role sitting across several modules. The work will challenge students to develop problem solving skills that enable them to approach unfamiliar as well as familiar problems. The students will also receive pastoral care and support for any problems, either academic or personal, that they encounter as a first-year student. The Small Group Tutor will fill the role of a personal tutor and help students with their transition to university life and studying at a higher level. If necessary, their tutor will direct students towards other UH sources of support.
Principles of Data Presentation	15 Credits	Compulsory
The module will begin with simple data presentation techniques such as graphical and numerical summaries and consider how these can be used to appropriately present data. The module will then develop these themes through the use of more advanced graphical summaries and data visualisation methods (including their production in a suitable statistical software package), the presentation of results in different media and the reporting of analyses (including the correct interpretation of results and discussion of modelling assumptions). The module develops skills in problem solving, IT skills, analysis and evaluation, and interpersonal and communication skills.

Level 5

Module	Credits	Compulsory/optional
Programming	15 Credits	Optional
Programming is an important skill in the modern working world. Using a modern high-level language, the module will cover control structures like loops and logical statements, as well as how to build more complex programs by writing and linking separate functions and procedures. You will learn how to debug programs and produce program documentation, and apply your skills to a mathematical or physical problem and computationally analyse the results. The module develops skills in computer programming, interpersonal and communication skills, and conceptualisation and critical thinking.
Professional Teaching Skills	15 Credits	Optional
The module aims to develop the practical skills and understand the role of a graduate in the sphere of education. By the end of the module students should appreciate the challenges, rewards and diversity of the educational sector. In this module you will develop your skills in team and organisational working, interpersonal and communication skills, information searching, adaptation to context, personal evaluation and development.
Investment Portfolios	15 Credits	Optional
This module investigates the construction and mathematical analysis of portfolios of securities. A variety of techniques from modern portfolio theory are introduced to analyse the risk and expected return of a portfolio and applied to the construction of optimal portfolios. Live financial market data will be downloaded from the internet and used in computing sessions to demonstrate the concepts and theories discussed in lectures. The students can create their own portfolio of assets and participate in a friendly competition to achieve the highest performing portfolio.
Financial Markets	15 Credits	Optional
The module investigates some of the most widely used theories and models relating to the mechanism of financial markets and the behaviour of investors. We will start by developing the single-index and multi-index models, and introduce two widely used asset pricing models, the Capital Asset Pricing Model and Arbitrage Pricing Theory. We will discuss investor behaviour, starting with traditional utility theory, and compare it to the emerging field of behavioural finance, introducing the idea that investors do not always act in a rational manner.
Numerical Methods	15 Credits	Optional
Numerical techniques are important across many areas of mathematical modelling, since it is rare that real-world problems admit solutions in closed form. In this module you will discover how to use numerical methods to solve mathematical problems. We will discuss the relative performance of different methods in terms of accuracy and efficiency, and investigate the theoretical background to the methods. We will cover methods for numerical integration, numerical solution of systems of linear equations, computation of eigenvectors and eigenvalues, and the numerical solution of systems of ODEs.
Game Theory	15 Credits	Optional
The purpose of this module is to introduce the fundamentals of game theory, including the concept of Nash equilibria. It focuses on various ways of describing and solving games, and their applications in real-world situations. The students will learn how to recognize and model strategic situations, to predict when and how their action will have an influence on others, and to exploit strategic situations for the benefit of their own.
Statistical Modelling	15 Credits	Compulsory
Statistical modelling refers to a set of tools for modelling and understanding complex data sets and plays a key role in many areas of science, finance and industry. This module has been developed to give students a detailed understanding of the commonly used statistical modelling techniques of analysis of variance and regression analysis. The module is supported by the use of a statistical computer package which facilitates the analysis of data sets, taken from a variety of application areas, using the methods taught in the lectures.
Career Planning and Development	0 Credits	Compulsory
The module aims to develop relevant skills and understand the role of a graduate in the sphere of work. By the end of the module students should appreciate the challenges, rewards and diversity of career planning and development. In this module you will develop your skills in team and organisational working, interpersonal and communication skills, information searching, adaptation to context, personal evaluation and development.
Information Theory	15 Credits	Compulsory
Information theory studies the fundamental limits of the representation and transmission of information. This course provides an introduction to information theory, studying fundamental concepts such as codes, information, complexity, and entropy and examining their applications in the areas of data compression, coding, communications, pattern recognition and probabilistic inference.
Big Data and High-Performance Computing	15 Credits	Compulsory
In this module you will learn how different types of large datasets are stored and represented and how you can use high-performance computing facilities to analyse them and extract key information. You will have access to the University's high-performance computing facility and will use it and other computing facilities to solve big data challenges during practical classes and independent study time. You will discover how to pose questions specifically related to large datasets and learn how to implement them computationally. Towards the end of the module, you will carry out a small independent project making use of high-performance computing facilities. You will be encouraged to seek out questions related to your own interests. The module develops skills in problem solving, computer programming, analysis and evaluation, and interpersonal and communication skills.
Blockchains and Cryptocurrencies	15 Credits	Compulsory
Blockchain technology is rapidly changing the financial industry and beyond. Countless applications are being explored in payments, insurance, lending, fund raising, settlement of securities transactions and contract execution. In this module we will explore what a blockchain is and how you can create one with simple Python codes. Cryptocurrencies, one of the most popular uses of blockchain, are explored in detail. The module develops skills in problem solving, computer programming, analysis and evaluation, and interpersonal and communication skills.
Data Science Laboratory 2	15 Credits	Compulsory
This module is laboratory based, with an emphasis on the use of key practical tools for data preparation, processing and analysis. Students will gain experience in developing data processing pipelines that exploit these tools and explore how distributed computing and GPUs can be used to scale and accelerate data engineering and processing tasks. Skills will be developed through a combination of hands-on workshops, where fundamental concepts will be introduced, and a series of challenges where students will be expected to develop their own solutions to a range of problems. The objective is to provide a solid grounding in the use of recognised programming platforms for data engineering and analysis and further develop general programming skills.
Introduction to Machine Learning and Neural Networks	15 Credits	Compulsory
This module provides an introduction on machine learning and artificial intelligence. It gives a broad but thorough overview of a range of modern methods and techniques used in machine learning with a particular emphasis on how to implement these techniques using an appropriate programming language.
Data Science Small Group Tutorial 2	0 Credits	Compulsory
This module is designed to give you academic support in small groups with studying the taught material from across your Level 5 modules. It also provides support and training for the Career Planning and Development module in the form of assistance with your personal skills audit, and guidance with putting together your CV. Your tutor group meets regularly during teaching periods. In meetings your tutor will facilitate discussions relating to tutorial activities, as well as in additional areas of study that you may suggest. You can also seek pastoral support from your tutor on any matters affecting your studies. The module develops skills in problem solving, interpersonal and communication skills, adaptation to context, and personal evaluation.

Level 6

Module	Credits	Compulsory/optional
Linear Modelling	15 Credits	Compulsory
This is a module in statistics for students who wish to study techniques widely used by statisticians. Linear modelling brings together regression and analysis of variance into a single modelling approach based on matrix algebra and extends these methods to include other types of response variables such as count and binary. The following topics are considered on this module: Analysis of Variance and Analysis of Covariance, Analysis of contingency tables by use of log-linear models, Logistic regression, Principles of the generalised linear model.
Multivariate Statistics	15 Credits	Compulsory
This is a module in statistics for students who wish to study techniques widely used by statisticians working in research, commerce and industry. Multivariate statistics introduces techniques for analysing more than one outcome variable simultaneously, and a number of multivariate statistical methods are introduced in this module. Throughout the module, computer packages are used wherever relevant to analyse case studies. The following topics are considered on this module: displaying multivariate data, multivariate normal distribution, multivariate T-tests, profile analysis, multivariate Anova, growth curves and discriminant analysis.
Data Science Project	30 Credits	Compulsory
In this module students will conduct a substantial research project. The project will tackle a particular topic in data science, where students will deploy their full range of skills to reach a level of depth of understanding and specialised knowledge not possible in individual modules. This will be an opportunity for students to develop independence, although collaboration in small groups is also encouraged to foster teamwork and collaboration. Students will be expected to take 'ownership' of their projects and bring their own enthusiasm and creativity to bear. To that end, although staff guidance will be provided, to help stimulate independent thinking students will be encouraged to propose their own projects. Collaboration with research-active staff from across the University will be welcomed, and it may be possible to conduct projects in collaboration with external companies.
Next Generation Data Science	15 Credits	Compulsory
This module provides an advanced knowledge on new and emerging concepts and techniques at the cutting-edge of data science. Topics covered include both hardware and software themes, for example exabyte computing, edge computing and the latest academic research into machine learning algorithms and AI. The module will explore how new tools and techniques could be relevant to real-world societal threats, such as pandemics and climate change.
Computer Vision and Natural Language Processing	30 Credits	Compulsory
This module explores fundamental techniques in computer vision and natural language processing. It will build on concepts already developed in the field of machine learning and AI (e.g. neural networks) and apply them to topics such as image recognition, object classification, optical character recognition, and speech segmentation.
Advanced Machine Learning and and Neural Networks	15 Credits	Compulsory
This module provides an advanced knowledge on machine learning and artificial intelligence, building on the concepts developed in earlier parts of the course. Students will learn about the underlying theory and fundamental mathematical/statistical principles, and get hands-on experience of creating, training, evaluating, and using various machine learning techniques, including deep neural networks.
Data Science Year Abroad	0 Credits	Optional
The Year Abroad module provides students with an exciting opportunity to expand, develop and apply the knowledge and skills gained in the first two years of their degree within a different educational environment, by studying for a year at a partner academic institution overseas. Students will experience the culture of the host country, and in some cases a new language. They will be able to study a variety of modules complementing those available at the University of Hertfordshire. Students who take a year abroad benefit from increased self-confidence and improved communication and interpersonal skills.
Data Science Placement With Study Abroad	0 Credits	Compulsory
Work experience on a placement provides students with an invaluable opportunity to understand how their academic studies fit into a broader context, to gain practical experience in relevant technical areas, and to strengthen their teamwork, IT, communication and time management skills. Studying abroad provides students with an exciting opportunity to expand, develop and apply the knowledge and skills gained in the first two years of their degree within a different educational environment, and to experience the culture of a different country. This module allows them to combine both elements, by spending at least 12 weeks on a placement and one Semester studying abroad. Students who have undertaken this module benefit from significantly increased self-confidence, independence and employability, improved communication skills, and a better understanding of the career they intend to pursue after graduation.

What if I need support?

You’ll have a personal tutor to guide you throughout your course, all the way to graduation. If you need additional support during your time with us, rest assured that we’ve got you covered. For help with study skills, including referencing, essay writing and presentations, you’ll have access to our academic support services. You can attend workshops, 1-to-1 sessions and online tutorials. Both our LRCs run drop-in study skills sessions. And the best thing is, it’s all free.

Where you'll study

Your main campus is College Lane, this is where the creative arts, science and health-related subjects are based. This means you’ll share the campus with future nurses, scientists, artists and more. You can use the common rooms to relax with friends, work out in the 24-hour gym or have a drink in our on-campus pub or cafes. We also have restaurants for you to eat in or grab something on the go. Our Learning Resources Centres are open 24/7, which means you can study whenever suits you best. Want to pop over to the other campus? You can take the free shuttle bus or walk there in just 15 minutes.

Learn in our new state-of-the-art building called Spectra, where you’ll experience a range of experiential learning zones. You will have access to Spectra's computer science labs, which offer telecommunications, robotics and UX empathy labs, alongside diverse research spaces including dark rooms, clean rooms, sample prep labs, calibration and assembly labs.

You will also benefit from our Academic Support Hub, which is aimed at helping you build your employability and academic skills. Plus, have access to industry mentors who will provide you with pastoral support, vocational guidance, and career progression opportunities.

Spectra also provides space to collaborate, with plenty of workshops, social and meeting spaces available. Even better, the building has been designed with the University’s net zero carbon target in mind, and forms part of our plan to replace or upgrade older sites that are energy inefficient.

Who you'll learn from

At Herts, your learning will be guided by a team of experts with extensive academic and industry experience. Many hold advanced degrees and are engaged in groundbreaking research, ensuring the course remains aligned with the latest developments in data science, machine learning, and AI.

Your teaching team incorporates real-world challenges and industry case studies into the curriculum, often drawing from collaborations with companies like Apple and Microsoft. Workshops and guest lectures provide further insight, preparing you with practical and future-ready skills for a career in STEM.

Check out our student blogs

How we support our students

At the University of Hertfordshire, we want to make sure your time studying with us is as stress-free and rewarding as possible.

We offer a range of support services, from childcare to counselling, ensuring that you make the most of your time at Herts and can focus on studying, having fun, and have the support you need.

Find out more about support and wellbeing

Entry requirements

The University of Hertfordshire is committed to welcoming students with a wide range of qualifications and levels of experience. The entry requirements listed on the course pages provide a guide to the minimum level of qualifications needed to study each course. However, we have a flexible approach to admissions and each application will be considered on an individual basis.

Typical offer (UCAS points)	(enter tariff range)
Typical A Level offer	(enter range)
Typical IB offer	(enter range)
Typical BTEC offer	(enter range)
Typical T Level offer	(enter range)
GCSE	EG: Grade 4/C in English Language and 4/D Mathematics
Access courses	EG: An overall merit profile in 45 credits at Level 3.
Additional information	Find out more about our standard and contextual Undergraduate Entry Requirements.

English language	All students from non-majority English speaking countries require proof of English language proficiency, equivalent to an overall IELTS score of 6.0 with a minimum of 5.5 in each band. If you do not have the required IELTS or equivalent for direct entry on to your degree programme, our Pre-sessional English and International Foundation courses can help you to achieve this level.
Additional information	Find out more about International Entry Requirements.

English language

All students from non-majority English speaking countries require proof of English language proficiency, equivalent to an overall IELTS score of 6.0 with a minimum of 5.5 in each band.

If you do not have the required IELTS or equivalent for direct entry on to your degree programme, our Pre-sessional English and International Foundation courses can help you to achieve this level.

Additional information

Find out more about International Entry Requirements.

Typical offer (UCAS points)	120–128
Typical A Level offer	BBB–ABB Mandatory subjects: Mathematics (grade B)
Typical IB offer	120–128 UCAS points and must include Mathematics at HL grade 4 or above
Typical BTEC offer	DDM Mandatory subjects: Applied Science with A Level Mathematics (grade C)
Typical T Level offer	None
GCSE	Grade 4/C in English Language and Mathematics.
Access courses	Diploma with 45 Level 3 credits, of which 15 must be in Mathematics at a minimum of merit
Additional information	Find out more about our standard and contextual Undergraduate Entry Requirements.

English language	All students from non-majority English speaking countries require proof of English language proficiency, equivalent to an overall IELTS score of 6.0 with a minimum of 5.5 in each band. If you do not have the required IELTS or equivalent for direct entry on to your degree programme, our Pre-sessional English and International Foundation courses can help you to achieve this level.
Additional information	Find out more about International Entry Requirements.

English language

All students from non-majority English speaking countries require proof of English language proficiency, equivalent to an overall IELTS score of 6.0 with a minimum of 5.5 in each band.

If you do not have the required IELTS or equivalent for direct entry on to your degree programme, our Pre-sessional English and International Foundation courses can help you to achieve this level.

Additional information

Find out more about International Entry Requirements.

Ready to apply?

UK and EU applicants with pre-settled/settled status in the UK

Start date	Study type	Apply
September 2025	Full time	Apply via UCAS
	Full time with placement year	Apply via UCAS
	Full time with study abroad year	Apply via UCAS
September 2026	Full time	Apply via UCAS
	Full time with placement year	Apply via UCAS
	Full time with study abroad year	Apply via UCAS

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Make sure you've got your UCAS number, if you've got one (you can find this on your UCAS Hub), your results (including GCSEs, A levels, BTECs or equivalent) and the course(s) you'd like to apply for.

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Click to call or dial 0300 303 6300

Find out more before you apply

Our Clearing hotline is open Monday – Thursday, 09:00 – 16:45, and Friday 09:00 – 15:45. To apply you will need your UCAS number, if you've got one (you can find this on your UCAS Hub), your results (including GCSEs, A levels, BTECs or equivalent) and the course(s) you'd like to apply for.

Fees and funding

At Herts, we’re dedicated to providing world-class teaching supported by industry-level facilities and incredible social spaces. We believe cost shouldn’t be a barrier to higher education, and we strive to keep both our standard and additional costs as low as possible.

	Study type	Fees
UK students	Full time	£9,535 for the 2025/2026 academic year
EU students	Full time	£15,965 for the 2025/2026 academic year
International students	Full time	£15,965 for the 2025/2026 academic year

Tuition fees are charged annually. The fees quoted above are for the specified year(s) only. Fees may be higher in future years, for both new and continuing students. Please see the University’s Fees and Finance Policy (and in particular the section headed “When tuition fees change”), for further information about when and by how much the University may increase its fees for future years.

Scholarships, grants and bursaries	Depending on your circumstances, you may be eligible for a non-repayable scholarship, grant or bursary to support your studies.
Disabled Students' Allowance	The Disabled Students’ Allowance can cover any study-related costs you have because of a mental health problem, long term illness or any other disability.
Student loans	Find out about securing a student loan, from how much you could be eligible for to when you need to start making repayments.
Accommodation costs	We offer a great choice of student accommodation, on campus or nearby in the local area, to suit every student budget.
Additional course fees	Read more about additional fees in the course fact sheet.

More about the course

Course fact sheets
BSc (Hons) Data Science	Download PDF

Programme specifications
BSc (Hons) Data Science	Download PDF

Additional information
Applications open to international and EU students	Yes
Course length	Part Time, 6 YearsSandwich, 4 Years

BSc (Hons) Data Science

Harness the future of tech with our data science degree. Develop expertise in data analysis, machine learning, and language processing. Gain experience solving real-world problems and prepare for a career in the rapidly growing field of data science.

Typical offer: 120–128

Fees: £9,535 per year (UK)

UCAS code: G200

Course length: Full time: three years

Location: University of Hertfordshire, Hatfield

Institute code: H36

Course overview

Why choose this course?

Your future career

See what recent graduates are up to

Heather Tymms

Heather Tymms

Studying at Herts with a disability

Life at Herts

Heather’s future career aspirations

Heather’s advice for other students

What you'll study

What if I need support?

Where you'll study

Who you'll learn from

Check out our student blogs

Dilek Selin - A Typical Day

Dilek Selin - Things you should know

Jack - A day in the life as a computer science student

How we support our students

At the University of Hertfordshire, we want to make sure your time studying with us is as stress-free and rewarding as possible.

Student Blogs

A typical day

Student Blogs

Things you need to know before studying Computer Science at Herts

Student Blogs

Facilities

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Entry requirements

UK applicants

International applicants

UK applicants

International applicants

Ready to apply?

UK applicants

UK and EU applicants with pre-settled/settled status in the UK

International applicants

Apply through Clearing

Fees and funding

Tuition fees

Additional information

More about the course

Further information

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