Taught course

Data Science

University of Hertfordshire · School of Physics, Engineering and Computer Science

Entry requirements

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.)

Months of entry

January, September

Course content

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 take a one-year paid industry placement. 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.
About the course
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.

Information for international students

Applicants whose first language is not English must demonstrate sufficient competence in English to benefit from the Programme. This is normally demonstrated by recognised awards equivalent to an overall IELTS score of 6.0. Candidates who do not satisfy these requirements will be considered on a case-by-case basis.

The programme is subject to the University's Principles, Policies and Regulations for the Admission of Students to Undergraduate and Taught Postgraduate Programmes (in UPR SA03), along with associated procedures. These will take account of University policy and guidelines for assessing accredited prior certificated learning (APCL) and accredited prior experiential learning (APEL).

Fees and funding

UK students
International students

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.

Qualification, course duration and attendance options

  • MSc
    part time
    36 months
    • Campus-based learningis available for this qualification
    full time
    12 months
    • Campus-based learningis available for this qualification

Course contact details

+44 (0)1707 284800