University of Leeds logo
  • Tuition Fee:
  • Local: $ 13.1k / 1
  • Foreign: $ 28.5k / 1
  • StudyQA ranking:
  • 30pts.
  • Duration:
  • 1 year

    Photos of university

    Training a new generation of leaders in Health Data Science, Biostatistics, Medical Statistics, and Epidemiology.

    This unique course is designed to train a new generation of data scientists with the specialist skills needed foranalysing and interpreting ‘big data’ in the settings of population health and healthcare delivery.

    The course is delivered by expert staff at the Leeds Institute for Data Analytics (LIDA) and is unique in the UK for specialising in the analysis of real-world health data for causal inference. LIDA is an internationally recognised centre for data science and recently partnered with the Alan Turing Institute, the UK's national institute for data science and artificial intelligence. Module lecturers Prof Mark Gilthorpe and Dr Peter Tennant are themselves Fellows of the Alan Turing Institute, in recognition of their world-leading contributions to health data science.

    The data revolution promises to transform our understanding of health and deliver new insights into the development of therapies and delivery of health care. Unlocking this potential requires a similar revolution in data analytic skills that combines new and emerging statistical skills with advanced scientific and critical reasoning. Our course provides the professional and technical skills training to support the development of a health research career in your chosen area.

    Course benefits

    • The UK’s only postgraduate course specialising in causal inference for ‘real world' health data.
    • Expert training in how to overcome pitfalls and malpractices of ‘real world’ data analysis (unique for postgraduate taught programmes world-wide).
    • State-of-the-art training in modelling for prediction and causal inference (unique for UK postgraduate taught programmes).
    • Extensive access to routine health and medical data maintained within Leeds Institute for Data Analytics (LIDA).
    • Novel and clinically-relevant project opportunities, supervised by research leaders, and leading to scientific manuscripts suitable for peer-reviewed publication.

    Career Opportunities

    Our course is designed for recent graduates with an interest in health data science including those seeking a career in quantitative health research, either within industry, the public sector, or academia, or advanced data analytics within a healthcare or health intelligence setting. Upon graduating you will be at the forefront of the discipline of health data science and have advanced knowledge and skills appropriate to a range of careers involving the analysis and interpretation of ‘real world’ health data.

    Recent graduate destinations include:

    • Medical Statistician, Institute for Cancer Research
    • Public Health Intelligence Analyst, Public Health England
    • Senior Analyst, NHS Digital
    • Senior Research Executive, Ipsos MORI
    • Data Consultant, The World Bank
    • Epidemiologist, Thrombosis Research Institute
    • Real Workd Data Scientist, GSK

    Many of our students also chose to study advanced postgraduate research degrees (such as PhDs and MDs) before going onto more senior positions in industry or the public sector.

    There is demand for students and academic staff with excellent statistical skills, an enquiring attitude, and a broader understanding of the research environment. Our graduates are attractive to employers in academic research, health and social care and industry. Prospective employers value their technical expertise and research understanding. They also value the professional skills gained, including writing a grant application and a paper of submission quality.

    This unique MSc offers an exciting blend of core and optional content to provide a cutting-edge grounding in modern health data science while allowing you to specialise in a range of areas, such as clinical trials, machine learning, spatial analytics, and genetic epidemiology.

    Our innovative Professional Skills for Health Data Analysts module will equip you with the skills and experience to work effectively in research, public health or health services research. It covers; ethics, academic writing for publication, consultancy, management and leadership skills.

    The course will also provide you with strong foundations in the skills and knowledge of data analytics with relevance to health. We stretch you to acquire and implement advanced techniques through optional modules. These modules allow your learning to be tailored towards discipline-specific paths appropriate to your future career.

    Compulsory modules

    • Introduction to Health Data Science
    • Statistical Inference for Health Data
    • Modelling Prediction and Causality with Observational Data
    • Further techniques in Health Data Analytics
    • Professional Skills for Health Data Analysts
    • Modelling Strategies for Causal Inference with Observational Data
    • Research Project

    Optional modules

    • Introduction to Clinical Trials
    • Introduction to Genetic Epidemiology
    • Latent Variable Methods
    • Independent Skills in Health Data Analytics
    • The Legal, Ethical and Professional Considerations in Healthcare Data Research
    • Machine Learning for Health Data
    • Spatial Analytics and Visualisation for Health

    UK requirements for international applications

    Universities in the United Kingdom use a centralized system of undergraduate application: University and College Admissions Service (UCAS). It is used by both domestic and international students. Students have to register on the UCAS website before applying to the university. They will find all the necessary information about the application process on this website. Some graduate courses also require registration on this website, but in most cases students have to apply directly to the university. Some universities also accept undergraduate application through Common App (the information about it could be found on universities' websites).

    Both undergraduate and graduate students may receive three types of responses from the university. The first one, “unconditional offer” means that you already reached all requirements and may be admitted to the university. The second one, “conditional offer” makes your admission possible if you fulfill some criteria – for example, have good grades on final exams. The third one, “unsuccessful application” means that you, unfortunately, could not be admitted to the university of you choice.

    All universities require personal statement, which should include the reasons to study in the UK and the information about personal and professional goals of the student and a transcript, which includes grades received in high school or in the previous university.


    University requirements


    Program requirements

    Entry requirements

    A bachelor degree with a 2:1 (hons) or equivalent qualification in a quantitative or scientific subject area with substantial mathematical, statistical or numeracy components. We also consider working experience (two years or more) of research in a quantitative subject area.

    English language requirements

    • IELTS 7.0 overall, with no less than 6.0 in writing and 6.5 in all other components.
    • TOEFL (Test of English as a Foreign Language) of 92 with no less than 21 in listening, 21 in reading, 23 in speaking and 22 in writing
    • Pearson (Academic) of 64 overall with no less than 60 in any component
    • Cambridge Advanced English (CAE), or C1 Advanced, of 176 overall with no less than 169 in any component
    • Trinity College Integrated Skills in English of a Pass in ISE II or above (if taken in the UK)
    • GCSE English Language or Cambridge IGCSE English as a First or Second Language at grade C

    Learning and teaching

    You’ll have access to the very best learning resources and academic support during your studies. We’ve been awarded a Gold rating in the Teaching Excellence Framework (TEF, 2017), demonstrating our commitment to delivering consistently outstanding teaching, learning and outcomes for our students.

    We mix face-to-face teaching with technology to enhance your learning experience. Self-directed online learning lets you study at a pace that suits you, whilst face-to-face support allows you to explore individual areas of difficulty and extend your understanding.

    You’re likely to experience:

    • small-group teaching with an expert in the field, including some modules with the opportunity to mix with students from other disciplines;
    • teaching in computer clusters to help you rapidly gain the skills required with statistical packages;
    • online workbooks with relevant links for further research;
    • online audio-visual presentations (vodcasts);
    • online help files and sample data sets with worked examples, which support all the statistical packages;
    • experiential learning as part of the research team for your research project;
    • continuous formative and summative assessment, and feedback.

    Assessment

    We understand the importance of assessment and feedback in your learning. We provide assessment in as many modules as possible so that you can gauge your understanding of the key concepts.

    You’ll get feedback in a variety of ways. These include:

    • informal discussion with tutors
    • written feedback from formative assessments
    • marks obtained in both formative and summative assessments
    • peer-review from presenting projects and data.

    Each module contains a summative assessment component. Some of these will be done via continuous in-course assessment, and some as end-of-module assessment.

    Our assessment and feedback will use a number of methods:

    • Online assessment which allows a flexible set of responses, marks the assessment immediately and provides both results and more structured feedback;
    • Short answer questions to test understanding of more complex methods and scenarios;
    • Project reports that allow deeper exploration of a topic;
    • Other methods to fit the skills and knowledge under test, eg presentation of data;
    • For the overall research project, regular meetings with your supervisor to monitor your progress and give feedback.
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