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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.
  • Entry requirements and key information

    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:  

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
    ModuleCreditsCompulsory/optional
    Applied Data Science 115 CreditsCompulsory
    Applied Data Science 215 CreditsCompulsory
    Data Science Project60 CreditsCompulsory
    Data Science Core Skills Bootcamp0 CreditsCompulsory
    Data Handling and Visualisation15 CreditsCompulsory
    Data Mining and Discovery15 CreditsCompulsory
    Research Methods In Data Science30 CreditsCompulsory
    Advanced Research Topics in Data Science30 CreditsCompulsory
    Fundamentals of Data Science30 CreditsCompulsory
    Machine Learning and Neural Networks30 CreditsCompulsory

    • 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

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