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  • Study Type

    Full time available

  • Subject Areas

    Medicine

  • Start Date

    October

  • Course Duration

    1 year

  • Course Type

    MRes

  • Course Fees

    2017/18 Home/EU £14,500 Overseas £30,600

  • Last Updated

    10 November 2016

Course content

Studentships

* One-year masters studentships are available for this stream. Each studentship will be worth £5000 and can be taken either as a reduction in fees or as a bursary. Studentships will be awarded based on academic merit and are open to all applicants, regardless of fee status (home/EU/overseas). Please indicate 'Data Science' in the first line of your personal statement.

* Two PhD Studentships targeted at successful graduates from this stream. Two 3-year PhD studentships will be on offer, targeted at students obtaining a minimum of a Pass with Merit on the Data Science stream. These studentships will cover the cost of tuition fees for home/EU applicants and a stipend at standard Research Council rates.

Stream overview

The Data Science stream provides an interdisciplinary training in analysis of ‘big data’ from modern high throughput biomolecular studies. This is achieved through a core training in multivariate statistics, chemometrics and machine learning methods, along with research experience in the development and application of these methods to real world biomedical studies. There is an emphasis on handling large-scale data from molecular phenotyping techniques such as metabolic profiling and related genomics approaches. Like the other MRes streams, this course exposes students to the latest developments in the field through two mini-research projects of 20 weeks each, supplemented by lectures, workshops and journal clubs. The stream is based in the Division of Computational and Systems Medicine and benefits from close links with large facilities such as the MRC-NIHR National Phenome Centre, the MRC Clinical Phenotyping Centre and the Centre for Systems Oncology. The Data Science stream is developed in collaboration with Imperial’s Data Science Institute.

Who is this course for?

Students with a degree in physical sciences, engineering, mathematics computer science (or related area) who wish to apply their numeric skills to solve biomedical problems with big data.

Stream Objectives

Students will gain experience in analysing and modelling big data from technologically advanced techniques applied to biomedical questions. Individuals who successfully complete the course will have developed the ability to:

• Perform novel computational informatics research and exercise critical scientific thought in the interpretation of results.
• Implement and apply sophisticated statistical and machine learning techniques in the interrogation of large and complex
biomedical data sets.
• Understand the cutting edge technologies used to conduct molecular phenotyping studies on a large scale.
• Interpret and present complex scientific data from multiple sources.
• Mine the scientific literature for relevant information and develop research plans.
• Write a grant application, through the taught grant-writing exercise common to all MRes streams.
• Write and defend research reports through writing, poster presentations and seminars.
• Exercise a range of transferable skills by taking short courses taught through the Graduate School and the core programme of the
MRes Biomedical Research degree.

Projects

A wide range of research projects is made available to students twice a year. The projects available to each student are determined by their stream. Students may have access from other streams, but have priority only on projects offered by their own stream. Example projects for Data Science include (but are not limited to):

• Integration of Multi-Platform Metabolic Profiling Data With Application to Subclinical Atherosclerosis Detection
• What Makes a Biological Pathway Useful? Investigating Pathway Robustness
• Bioinformatics for mass spectrometry imaging in augmented systems histology
• Processing of 3D imaging hyperspectral datasets for explorative analysis of tumour heterogeneity
• Fusion of molecular and clinical phenotypes to predict patient mortality
• 4-dimensional visualization of high throughput molecular data for surgical diagnostics
• Modelling short but highly multivariate time series in metabolomics and genomics
• Searching for the needle in the haystack: statistically enhanced pattern detection in high resolution molecular spectra

Visit the MRes in Biomedical Research (Data Science) page on the Imperial College London web site for more details!

Visit the MRes in Biomedical Research (Data Science) page on the Imperial College London website for more details!

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Recipient: Imperial College London

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