BSc (Hons) Data Science

Key information

Typical offer:
Entry requirements
Fees: See below
Full details
UCAS code: G200
Institute code: H36
Study abroad option

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.

UCAS points	A Level	BTEC	Access Course Tariff	IB requirement
120-128	BBB-ABB	DDM – Applied Science AND A level Maths grade C	Diploma with 45 Level 3 credits of which 15 must be in maths at a minimum of Merit	IB 120-128 points must include maths at HL grade 4 or above

Additional requirements

Additionally: GCSE grade 4 in English Language and 4 in Mathematics

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.

For more details on the University of Hertfordshire's entry requirements, please visit our Undergraduate Entry Requirements page.

Find out more about International Entry Requirements.

UCAS points	A Level	BTEC	Access Course Tariff	IB requirement
120-128	BBB-ABB with a minimum of grade B in maths	DDM – Applied Science AND A level Maths grade C	Diploma with 45 Level 3 credits of which 15 must be in maths at a minimum of Merit	IB 120-128 points must include maths at HL grade 4 or above

Additional requirements

GCSE grade 4 in English Language and 4 in Mathematics

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.

For more details on the University of Hertfordshire's entry requirements, please visit our Undergraduate Entry Requirements page.

Find out more about International Entry Requirements.

Why choose this course?

Data is one of the most important currencies at this current time and it underpins our modern world, from the flow of data across international banking networks, to the spread of memes across social networks, to the complex models of weather forecasting. The constant generation of data from our digital society feeds into our everyday lives, affecting how we receive healthcare to influencing our shopping habits. Demand for competent Data Scientists is growing across an extraordinarily diverse range of sectors, from finance to health to retail. However, there is a skills gap that urgently needs addressing, and this requires dedicated, specialised professionals who will fill Data Science and related roles, now and in the future.

Careers and employability 

According to Indeed, the average salary of a Data Scientist in the UK is £50,538 (according to 2600 salaries as of August 2021), approximately 60% higher than the average salary in the UK.

Data Science skills are relevant to a wide range of sectors, from healthcare to finance to retail and beyond. Demand is expected to grow in the coming years, with data science becoming embedded throughout the UK and global economy.

What's the course about?

The programme is distinctive in its combination of fundamental mathematical/statistical skills, an emphasis on holistic industry-relevant skills and inclusion of cutting-edge topics in Data Science (drawing on the University of Hertfordshire’s world-leading research, such as neuromorphic computing and the detection of exoplanets). This programme caters to students who are looking to gain important specialised skills before entering the workplace but also have a solid foundation in the underlying theoretical principles. There is a very strong emphasis on hands-on learning, with students engaging with learning material through practical laboratories (Data Science Laboratory 1 & 2). This is designed not just to help embed classroom-based learning, but also to instil confidence and independence in students’ technical capabilities, which will mature to a professional standard and encompass an increasing range of skills as the course develops. We also aim to ensure students develop independence in their analytical and critical thinking, bringing their own initiative and creativity to solve problems.

BSc Data Science students will also be offered the opportunity to attend the wide range of research-focused seminars and colloquia running during the teaching term. For example, the Centre for Astrophysics Research weekly astrophysics seminar, the Computer Science colloquium, or the Maths-Physics seminar. Current MSc and PhD students attend, and benefit, from these seminars, and – although not mandatory – BSc students will be encouraged to attend talks of interest and be made welcome. This is aimed to promote an atmosphere of excitement and intellectual curiosity, adding to the richness of the programme, as well as a feeling of inclusion for the student body. 

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. 

New School of Physics, Engineering and Computer Science building opening in 2024

Learn in our brand-new building, where you’ll experience a range of experiential learning zones.

The computer science labs are home to telecommunications, robotics and UX empathy labs, with a variety of research spaces that range from dark rooms to clean rooms, and sample prep labs to calibration and assembly labs.

You will also benefit from a Success and Skills Support Unit, 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.

The new building will also provide 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.

What will I study?

Classroom-based learning, online material, on-campus computer lab-based activities, directed and self-directed independent study.

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 (1) the key principles of data engineering, such as dealing with noisy and missing data, (2) design and implement algorithms and data analysis pipelines to tackle a variety of problems in the most efficient manner, and (3) 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
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 , e.g. through the use of databases. 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.
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.

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. In this module students will: • Have guest lectures from research-active members of UH and beyond, such as the biocomputation group in SPECS • Have guest lectures from industry experts, for example those working in hardware design or data centre innovation • Have an opportunity to explore the latest computer science literature, including use of the arXiv pre-print server By definition the course content is expected to be dynamic and be updated in line with (perhaps unforeseen) technical and theoretical developments in the field.
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	Compulsory
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 in 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.

Study abroad

Yes, this course has the option to study abroad via BSc Data Science With Year Abroad, or BSc Data Science With Study Abroad and Employment Experience.

Please note Erasmus+ funding is only available until May 2023. For students starting their course in September 2022 and wishing to study abroad in 2023-24 or 2024-25, please refer to the Turing Scheme.

Alumni Stories

Heather Tymms

Meet Institute of Mathematics prize-winner Heather, who loved her time at Herts and was determined not to let her sight-loss become a barrier to getting fully involved with student life and achieving a first-class degree.

Read more stories BSc (Hons) Mathematics

Current job role	Data Science and Analytics Advisor for GHD
Year of graduation	2022
Course of study	BSc (Hons) Mathematics with Placement Year

Studying at Herts with a disability

Heather hadn’t planned to go to university straight after A-Levels as she lacked the confidence. However six years ago she lost the majority of her sight and wanted to find her independence again. This was when she decided she needed a challenge and a new qualification. Her journey has led her to achieving a first-class degree in Mathematics, winning a school prize from the Institute of Mathematics and its Applications.

Heather says, ‘I can’t believe how far I have come. I have grown so much, and the future is brighter than I ever imagined it to be. When I started at Herts I would never have dreamt of being able to commute to London. Herts has given me the confidence to achieve more and provided me with the skills and ambition I needed to equip myself for a professional career in a fascinating sector!’

Heather chose to study at Herts as it was her local university and because she knew she would be able to get the support she needed.

‘Being new to having a disability, I needed some extra help and guidance along the way. The Wellbeing Team at Herts provided me with lots of support and resources. Before I started, I was taken on a tour to familiarise myself with the campus so I could learn the best routes between my lectures. This was very useful, as I can get anxious in new places. I also had help in the lectures because I couldn’t see the board! My lecturers were very accommodating and changed timetabled classrooms to improve access for me if required. I was able to claim the Disabled Students' Allowance, which enabled me to buy a specialist laptop with assistive technology and a magnifier.’

‘I stayed closely connected with my lecturers and the Wellbeing Team through my studies, and I was able to help improve the experience of other disabled students on campus. For example, working with university staff on signage and communication around building sites to enhance safety measures. The university staff were always happy to help improve the safety of all their students.’

‘I really benefited from the support I received from my school careers advisor. Through this service, I had help to secure both a placement and graduate job. I received expert practical advice to help me apply for jobs, they helped improve my confidence with job interviews, and showed how I could disclose my disability to employers in a covering letter.’

Life at Herts

Heather had a brilliant student experience and got involved with lots of extra-circular activities.

‘I really enjoyed my course, I found the lecturers extremely friendly, knowledgeable, and approachable, something which I really valued. I also enjoyed meeting and working with people from all different backgrounds. Herts is a truly diverse and inclusive place to study, and I benefited from collaborating with so many different people.

‘My favorite place on campus was the library. It has such a good atmosphere, and this was where I could concentrate on course work with friends.’

‘I also got involved with the Physics, Astronomy and Maths Society (PAMsoc) and in my final year, I was the society chair. Through PAMsoc activities I was able to learn and make friends with like-minded people, enriching my student experience. We did quizzes, pub nights, discussion groups, and invited our lecturers to speak to us about their personal career paths and the cutting-edge research they are doing.’

Heather’s future career aspirations

Heather is passionate about Maths and says, ‘You can do so many different things with a Maths degree, and not just teaching. You learn transferable skills, critical thinking, elements of computer science, and physics.’

‘I was introduced to the data sector in my placement year at Sainsbury's Digital, where I got to work on interesting projects like using data analysis to improve the experience of customers at the supermarket checkout. I now work for an Engineering Consultancy, GHD, a company who are committed to solving global challenges in the areas of water, energy and urbanisation, values which align to my own. I do a job that I love using data models and python, skills learnt on my course.’

‘I do miss academia sometimes and I may like to do further study in the future.’

Heather’s advice for other students

‘At Herts, you can learn about your chosen subject, but also learn about yourself. Make sure you take the time to reflect and understand both you and your passion throughout your studies and take every opportunity you can. Say yes to new things but know your own limit and don’t burn yourself out. At Herts, there is always support available to you, so seek out help if you need it.’

View our Alumni profiles

Heather Tymms

Course fact sheets
BSc (Hons) Data Science	Download

Programme specifications
BSc (Hons) Data Science	Download

Additional information
Sandwich placement or study abroad year	Optional
Applications open to international and EU students	Yes
Course length	Part Time, 6 Years Sandwich, 4 Years
Location	University of Hertfordshire, Hatfield

2024

Start Date	End Date	Link
26/09/2024	31/05/2025	Apply online (Part Time)
26/09/2024	31/05/2025	Apply online (Full Time/Sandwich)

2025

Start Date	End Date	Link
26/09/2025	31/05/2026	Apply online (Part Time)
26/09/2025	31/05/2026	Apply online (Full Time/Sandwich)

Fees and funding
Fees 2024
UK Students

Full time
- £9250 for the 2024/2025 academic year
Part time
- £1155 per 15 credits for the 2024/2025 academic year
EU Students

Full time
- £15500 for the 2024/2025 academic year
Part time
- £1940 per 15 credits for the 2024/2025 academic year
International Students

Full time
- £15500 for the 2024/2025 academic year
Part time
- £1940 per 15 credits for the 2024/2025 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.

View detailed information about tuition fees

Read more about additional fees in the course fact sheet

Other financial support

Find out more about other financial support available to UK and EU students

Living costs / accommodation

The University of Hertfordshire offers a great choice of student accommodation, on campus or nearby in the local area, to suit every student budget.

View detailed information about our accommodation

Book a campus tour

Get info and updates

BSc (Hons) Data Science

Key information

Typical offer:

Fees: See below

UCAS code: G200

Institute code: H36

Study abroad option

Additional requirements

Additional requirements

Why choose this course?

What's the course about?

Your main campus is College Lane

New School of Physics, Engineering and Computer Science building opening in 2024

What will I study?

Alumni Stories

Meet Institute of Mathematics prize-winner Heather, who loved her time at Herts and was determined not to let her sight-loss become a barrier to getting fully involved with student life and achieving a first-class degree.

Studying at Herts with a disability

Life at Herts

Heather’s future career aspirations

Heather’s advice for other students

2024

2025

Fees 2024

UK Students

Full time

Part time

EU Students

Full time

Part time

International Students

Full time

Part time

Other financial support

Living costs / accommodation

Your main campus is College Lane 