* 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.
This course is a stream within the broader MRes in Biomedical Research.
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.
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.
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.
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!
Master of Research (Biomedical Science) – MRes / PGCert
Master of Research (Psychology) – MRes / PGCert
Master of Research (Health) - MRes/PGCert
The Master of Research (MRes) programme provides high quality and professionally relevant training in research methods and analysis for health, psychology and biomedical science graduates.
Through the taught modules students will develop their research skills, including critically appraising literature, developing research proposals, laboratory techniques, project management, presenting findings (through oral, written and digital media) and applying for funding. Students will be introduced to a wide range of methodologies and practical techniques relevant to their discipline. Training will be provided in research governance and ethical procedures to ensure students are prepared for work as professional researchers.
Students will conduct two substantial research studies in a topic of their choice, supervised by experienced members of academic staff. These studies will be written up in the form of journal articles and will form a substantial component of the credits for the MRes award.
This award prepares you for a future in research and progression on to PhD studies.
Students will complete the following modules over the course of one full year (full-time) or two years (part-time); modules can be viewed HERE.
There are subject specific and three shared modules which prepare you for your area of research and develop your skills as a researcher.
Students will complete a dissertation proposal (10 credits) and associated ethics application. This is followed by the dissertation which counts for 100 credits. The dissertation is written as two research papers which address two discrete areas of the research project. The dissertation will involve all the elements of the research process from the design of the study, data collection, analysis of findings and write up in the format of a journal article. Students will be supervised by two members of staff to complete their dissertation. Students will be matched with appropriate staff members who have expertise in the chosen topic and/or methodology.
The MRes programme comprises 180 credits of which 110 are gained from completing a dissertation proposal and dissertation on the students chosen topic. Therefore the programme is largely independent research, with the support of academic supervisors. The 70 credits of specialised research taught modules ensure you will be equipped with the knowledge, practical and professional skills to engage in your chosen research area and also to provide you with a broader knowledge base of research methodologies. The taught based modules will involve a variety of different learning and teaching methods including: lectures, seminars, small group work, workshops, online learning, tutorials and self-directed learning.
Each student will be allocated two members of staff to supervise their dissertation and a separate personal tutor. Your personal tutor will support you with pastoral care and advice for career planning, e.g. supporting you to complete a personal development plan.
Each module has its own form of assessment. The majority of modules are assessed via coursework, including research reviews, reports, essays and presentations. The dissertation will be written up in the format of two journal articles.
MRes graduates will be well placed for PhD level study or a career that involves carrying out, critically appraising or applying research findings. Evidence based practice means that a knowledge and understanding of research is essential in all health related domains so that health care providers are able to interpret previous research findings, as well as contributing to the process of designing and carrying out research projects. Graduates will also be well equipped in roles which involve scientific writing, analysis of research, designing or administering research projects and development of research portfolios.