MPhil in Data Intensive Science
University of Cambridge
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Qualification

MPhil - Master of Philosophy

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Location

University of Cambridge

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Study mode

Full Time

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Start date

OCT-25

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Duration

10 months

Course info

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Qualification

MPhil - Master of Philosophy

Location

University of Cambridge

Study mode

Full Time

Start date

OCT-25

Duration

10 months

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Computer Science
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Course info

The MPhil in Data Intensive Science is a 10-month cross-departmental programme in the School of the Physical Sciences which aims to provide education of the highest quality at the master’s level. The programme covers the full range of skills required for modern data-driven science from the fields of machine learning and AI, statistical data analysis, and research computing.The course structure has been designed in collaboration with our leading researchers and industrial partners to provide ...Read more

The MPhil in Data Intensive Science is a 10-month cross-departmental programme in the School of the Physical Sciences which aims to provide education of the highest quality at the master’s level. The programme covers the full range of skills required for modern data-driven science from the fields of machine learning and AI, statistical data analysis, and research computing.

The course structure has been designed in collaboration with our leading researchers and industrial partners to provide students with the theoretical knowledge, practical experience, and transferable skills required to undertake world-leading data-intensive scientific research. Students will gain the broad set of skills required for scientific data analysis, covering traditional statistical techniques as well as modern machine learning approaches. Both the theoretical underpinnings and practical implementation of these techniques will be taught, with the later aspect including training on software development best practice and the principles of Open Science. The course also aims to provide students with direct experience applying these methods to current research problems in specific scientific fields. Students who have completed the course will be equipped to undertake research on data-intensive scientific projects. Beyond academic disciplines, students will be well prepared for a career as a data science professional in a broad range of commercial sectors.

This course will equip students with all the skills required for modern scientific data analysis, enabling them to participate in large experimental or observational programmes using the latest statistical and machine learning tools deployed on leading-edge computer architectures. These computational and statistical skills will also be directly applicable to data-driven problem-solving in industry.

The course responds to the growing:

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    • demand for highly trained research scientists to design and implement data analysis pipelines for the increasingly large and complex data sets produced by the next generation of scientific experiments;
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    • societal demand for data science and data analysis skills in the industry, especially when applied in strategic domains (science, health) and economic areas (finance, e-commerce);
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    • need to train postgraduate students with a deep understanding of data science techniques and algorithm building for modern computer architectures and utilising industry best practices for software development;
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    • importance of open science in research, specifically reproducibility of scientific results and the creation of public data analytic codes.
    • Learning Outcomes

      By the end of this course, students will have:

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        • thorough knowledge of statistical analysis including its application to research and how it underpins modern machine learning methods;
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        • comprehensive understanding of data science and machine learning techniques and packages and their application to several practical research domains;
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        • developed advanced skills in computer programming utilising modern software development best practices created in accordance with Open Science standards;
        • demonstrated abilities in the critical evaluation of data science tools and methodologies for their real-world application to scientific research problems.

Key stats

CUG Subject Ranking
CUG Subject Ranking
Source: Complete University Guide 2025
1st
CUG Ranking
CUG Ranking
Source: Complete University Guide 2025
1st

Entry requirements

Applicants for this course should have achieved a UK High II.i Honours Degree.

Tuition fees

Students living in

Domestic

£13,554 per year

This information is updated by IDP Connect, or in some cases the institution directly.
Please note: The fees might vary so please make sure you contact the institution for up to date information.

Students from Domestic

This is the fee you pay if the University is in the same country that you live in (England, Scotland, Wales, Northern Ireland)

University Composition Fee: £13,554; Maintenance: £15,521.

£35,526 per year

This information is updated by IDP Connect, or in some cases the institution directly.
Please note: The fees might vary so please make sure you contact the institution for up to date information.

Students from EU

The amount you'll pay if you come to study here from somewhere in the EU.

University Composition Fee: £35,526; Maintenance: £15,521.

£35,526 per year

This information is updated by IDP Connect, or in some cases the institution directly.
Please note: The fees might vary so please make sure you contact the institution for up to date information.

Students from International

The amount you'll pay if you come to study here from a country outside the EU.

University Composition Fee: £35,526; Maintenance: £15,521.

IDP Connect

Uni info

University of Cambridge
The Old Schools Trinity Lane Cambridge Cambridgeshire CB2 1TN United Kingdom
Nearest train station: Cambridge  0.6 miles away

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