MSc Data Science with Advanced Research

Why choose Herts?

Teaching excellence: You will be taught by internationally recognised research staff with expertise across mathematics, statistics, astrophysics, medical physics, and computer science (see key staff)
Work placement opportunities: You have an option to undertake a placement of up to one year conditional on course requirements. Placements may be paid or unpaid depending on the employer organisation. With the support from the Career Service at Herts, and the departments own Careers Advisor, you will look for your preferred placement during your first year of studies. Students have had placements with organisations including NatWest, Sparta Global and Sky
Industry connections: Benefit from our strong links with the computing industry. We work with employers such as Microsoft and Hewlett Packard for students to engage in careers fairs and industry sessions.

For Near-STEM students, the normal entry requirements for the programme are a good (2:1 or above) Honours Degree (or equivalent) in a STEM subject (e.g. Mathematics, Engineering, Physical Sciences etc). If a student has a relevant STEM degree, then they will be considered ‘Near-STEM’. They will be offered the opportunity to participate in the Data Science Core Skills bootcamp but will not be required to participate/attend.

For Far-STEM students, the normal entry requirements for the programme are a good (2:1 or above) Honours Degree (or equivalent). The subject of the degree is not defined, since (a) many different, disparate subjects might have a Data Science relevance (e.g. Business, Geography), and (b) some students might possess a non- STEM degree but have relevant experience (e.g. from employment). For far-STEM students who do not possess a good Honours Degree or equivalent, applications will be assessed on a case-by-case basis. Applicants may be asked to submit a short portfolio providing evidence of:

A basic level of numeracy (e.g. GCSE maths)
Experience and competency with IT / software (e.g. use of Microsoft Excel)
Experience of a basic interaction with data of any form (e.g. inputting values, making calculations, examining imaging, etc.)


Institution code	H36
School of study	School of Physics, Engineering and Computer Science
Course length	Full Time, 2 Years
Location	University of Hertfordshire, Hatfield

Employability
Careers

Upon completion of the programme you will be able to demonstrate (and apply) an understanding of a wide range of theoretical and practical skills enabling you to enter a variety of disciplines and industries. You will be able to:
- Understand and be able to critically assess the various strengths and weaknesses inherent to different data science methodologies
- Design creative strategies and solutions to tackle unfamiliar data science problems and critically assess outputs and results through appropriate statistically robust validation and other performance assessment techniques
- Effectively communicate problems, methods, results and conclusions through oral and written presentation to both expert and non-expert audiences
- Have an appreciation of both the underlying research behind data science techniques (e.g. cutting-edge algorithms and computational techniques) and their relevance and application across a broad range of disciplines.

Professional Accreditations

Accredited by BCS, The Chartered Institute for IT for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional.

About the course

The programme offers three award routes that you can choose to study:

MSc Data Science
MSc Data Science with Placement Year
MSc Data Science with Advanced Research

Data is the currency of all but the most theoretically-based scientific research, and it also underpins our modern world, from the flow of data across international banking networks and 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. In order to handle, make sense of, and exploit large volumes of available data requires highly skilled human insight, analysis and visualisation. The professionals working in this field are called ‘data scientists’, who blend advanced mathematical and statistical skills with programming, database design, machine learning, modelling, simulation and innovative data visualisation. These professionals are in high demand in both public and private sectors in the UK and worldwide. This programme aims and learning outcomes are built around two guiding principles:

To provide comprehensive understanding of the fundamental mathematical and statistical concepts underlying data science, and how they are implemented in algorithms and machine learning techniques to solve a variety of data processing and analysis problems.
To provide training in the practical skills relevant to data science, central of which is the ability to write clean and efficient code in industry-recognised languages (in particular, Python and R), but also includes data handling, manipulation, mining and visualisation techniques.

Why choose this course?

3rd for computer science in the Postgraduate Taught Experience Survey (PTES, Advance HE, 2023).
This programme is distinctive in its philosophy of widening participation and provides a route to gain skills and training in data science to those from a background not traditionally associated with the STEM-themes of mathematics, statistics and programming. The programme is designed to be appealing to a broad range of students who are seeking training or up-skilling in data science.
You will benefit from the expertise of astrophysicists, physicists, mathematicians and computer scientists with international research profiles. Their day-to-day research involves application of, and in some cases the development of new, data science skills, from fundamental statistical analyses, the use of distributed high-performance computing, and research into novel artificial intelligence algorithms.
We aim to make the programme distinctive in terms of the mixture of hard and soft skills, and the close personal relationship that we are developing with employers, which will feed into the programme through continuous assessment of the latest industry-relevant tools, which are continually evolving as new technology and software becomes available.
You will experience a multidisciplinary approach to data science by experiencing challenges in computer science, creative arts, medical and business environments.
You will have the opportunity to attend a wide range of research-focused seminars to excite and spark your intellectual curiosity.
Computer Science saw 90% of its research ranked as world-leading (Research Excellence Framework, 2021).

What will I study?

The curriculum is structured to ensure are exposed to the fundamental mathematical and statistical principles underpinning all data science. These themes will always be relevant in what is a constantly evolving field. Theoretical work will be reinforced with practical application through hands-on laboratories and workshops, to enable you to understand and appreciate how fundamental principles are reflected in a broad range of data processing and analyses. You will become proficient in key practical skills (e.g. use of pandas for working with data structures within Python, and ggplot2 for visualisation in Python and R) using ‘real-world’ data where possible. In some cases, this data can be sourced from active research projects being conducted by members of the teaching staff.

The programme focuses on providing ‘end-to-end' training so that you become competent not only in the processing and analysis of data but also in manipulating and preparing data from a raw state as well as interpreting results and effectively communicating findings to others. This will enable you to be prepared for real-world challenges and applications and will help you to develop independence in your analytical and critical thinking. This will be nurtured in laboratory-based practical sessions so you can put your theories into practice.

Where will I study?

Learn in our new School of Physics, Engineering and Computer Science building (SPECS), 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 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.

Level 7

Module	Credits	Compulsory/optional
Applied Data Science 1	15 Credits	Compulsory
This module is laboratory based, providing students with hands"on training in several techniques fundamental to data science. It provides an environment where students have an opportunity to improve 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 10 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.
Applied Data Science 2	15 Credits	Compulsory
This module is laboratory based, with an emphasis on the use of three key practical tools for data preparation and processing: Apache Spark, Tensorflow and Keras. 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 that incorporate the use of Spark, Tensorflow and Keras. 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.
Data Science Project	60 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.
Data Science Core Skills Bootcamp	0 Credits	Compulsory
In this intensive, non"assessed 'bootcamp' module students will become familiar with the core mathematical, statistical and programming concepts that underpin MSc Data Science. Very little prior knowledge is assumed for this module, which aims to provide a solid grounding in the key basic skills. The curriculum includes: basic algebra and mathematical concepts (e.g. functions), vectors and matrices, linear algebra, differentiation, statistical distributions, probability, and basic programming using the Python language. The delivery of the module content comprises of a set of online lectures, tutorials and problem sets, where students will gain familiarity with the central topics. There are 3 handbooks, one for each topic (maths, statistics and programming), containing notes to guide the learning. Students can check their progress on the module through a series of online formative quizzes. The module will be completed by students in self"directed study.
Data Handling and Visualisation	15 Credits	Compulsory
In this module, which combines lectures with practical workshops, students will learn the key principles and practical skills involved in (a) ingesting, manipulating and processing various forms of data, including binary and ascii formats, and (b) the use of graphic visualisation to explore data sets, for example to discover trends and correlations and other relationships within multi" dimensional data. Students will gain hands"on experience in producing effective visualisation using a range of presentation techniques to communicate findings effectively. This will include the use of versatile practical tools including NumPy, pandas, matplotlib. Students will consolidate learning through practical workshops where real"world data will be used as a basis for applying their skills.
Data Mining and Discovery	15 Credits	Compulsory
In this module students will learn how to retrieve and 'mine' information from databases and other large data sources. Students will gain an understanding of SQL and noSQL databases and how to access their contents. Part of the module will focus on data engineering' methods such as cleaning that are essential for preparing data for onward processing. Students will also learn how to prepare high"level quantitative and graphic summaries of large datasets. There will be an emphasis on methods to identify trends and anomalies within large datasets, and the use of association rules to discover relationships between data items. Lectures will be supported by practical workshops, where real"world data will be used to enable students to put into practice the techniques discussed in class.
Research Methods In Data Science	30 Credits	Compulsory
This module explores a range of generic and domain-specific research methods in data science to enable students to understand such methods and apply them in their work, particularly in an advanced MSc project or a research project. The module helps students to enhance their proficiency in the skills that are expected of those working at postgraduate level. Whilst some material will be presented in lectures, tutorials and labs, the module will be largely literature- and activity-based. It will place strong emphasis on self-management and will encourage students to reflect upon, and learn from, their own work. As the module progresses students will be expected to select an increasingly large proportion of the reading matter for themselves, so that they can tailor their learning to their individual needs in which they evaluate and choose an appropriate set of research methods for the investigation of a problem in a given sub-domain of computer science, and identify the principal advantages and limitations of those methods.
Advanced Research Topics in Data Science	30 Credits	Compulsory
This module aims to develop research and enquiry capabilities and is therefore structured around guided activities in terms of seminars, tutorials and research oriented courseworks. The module will build on introductory research experience from core modules and provide a structured approach to develop area specific expertise by undertaking a research activity in a chosen area of specialisation and in a group setting that mirrors contemporary research methods. A concrete outcome is a technical report that synthesizes problem definitions from relevant literature and identifies a research problem in a chosen topic.
Fundamentals of Data Science	30 Credits	Compulsory
In this module the student will study a selection of topics centred around basic mathematical concepts and skills, typically including basic linear algebra, calculus, basic statistics, fundamentals of probability, basic set theory and information theory. Students will use computational techniques to code some of the mathematical foundation. Students will learn how to select an optimal approach and implement it as a computational algorithm for solving typical data science problems. Actual topics taught may vary from year to year.
Machine Learning and Neural Networks	30 Credits	Compulsory
A study of a selection of research topics centered around neural network theory and design, machine learning including supervised and unsupervised learning and some interesting applications, for example, data mining, biocomputation, evolutionary algorithms, neural networks as models of brain function in health, disease and development, and data visualization. Actual topics taught may vary from year to year.

Key staff

Dr Ashley Spindler
Senior Lecturer
Find out more about Dr Ashley Spindler

Dr Carolyn Deveraux
Programme Leader and Principal Lecturer
Find out more about Dr Carolyn Deveraux

Dr John Evans
Lecturer
Find out more about Dr John Evans

Dr Mykola Gordovskyy
Senior Lecturer
Find out more about Dr Mykola Gordovskyy

Dr Vidas Regelskis
Senior Lecturer
Find out more about Dr Vidas Regelskis

Dr William Alston
Senior Lecturer
Find out more about Dr William Alston

Further course information

Course fact sheets
MSc Data Science - Extended	Download

Programme specifications
MSc Data Science - Extended	Download
MSc Data Science - Extended	Download

Additional information
Sandwich placement or study abroad year	n/a
Applications open to international and EU students	Yes

Student experience

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 including; student wellbeing, academic support, accommodation and childcare to ensure that you make the most of your time at Herts and can focus on studying and having fun.

Find out about how we support our students

You can also read our student blogs to find out about life at Herts.

Funding and fees
Other financial support

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

Fees 2024
UK Students
Full time
- £13545 for 2024/2025 and 2025/2026 inclusive
EU Students
Full time
- £18950 for 2024/2025 and 2025/2026 inclusive
International Students
Full time
- £18950 for 2024/2025 and 2025/2026 inclusive
Fees 2025
UK Students
Full time
- £14400 for 2025/2026 and 2026/2027 inclusive
EU Students
Full time
- £19800 for 2025/2026 and 2026/2027 inclusive
International Students
Full time
- £19800 for 2025/2026 and 2026/2027 inclusive
*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

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

Read more about additional fees in the course fact sheet
How to apply

International/EU applicants without pre-settled status in the UK

Apply through our international/EU application portal

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

2024
Start Date End Date Year Location Link
22/01/2025 31/01/2026 1 UH Hatfield Campus Apply online (Full Time)
2025
Start Date End Date Year Location Link
25/09/2025 31/05/2026 1 UH Hatfield Campus Apply online (Full Time)
22/01/2026 31/01/2027 1 UH Hatfield Campus Apply online (Full Time)
2026
Start Date End Date Year Location Link
25/09/2026 31/05/2027 1 UH Hatfield Campus Apply online (Full Time)
22/01/2027 31/01/2028 1 UH Hatfield Campus Apply online (Full Time)

Start Date	End Date	Year	Location	Link
25/09/2025	31/05/2026	1	UH Hatfield Campus	Apply online (Full Time)
22/01/2026	31/01/2027	1	UH Hatfield Campus	Apply online (Full Time)

Start Date	End Date	Year	Location	Link
25/09/2026	31/05/2027	1	UH Hatfield Campus	Apply online (Full Time)
22/01/2027	31/01/2028	1	UH Hatfield Campus	Apply online (Full Time)

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MSc Data Science with Advanced Research

Why choose Herts?

Careers

Professional Accreditations

About the course

Why choose this course?

What will I study?

Where will I study?

Key staff

Further course information

Student experience

Other financial support

Fees 2024

UK Students

Full time

EU Students

Full time

International Students

Full time

Fees 2025

UK Students

Full time

EU Students

Full time

International Students

Full time

Living costs / accommodation

International/EU applicants without pre-settled status in the UK

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

2024

2025

2026