Applied Statistical Modelling & Health Informatics MSc, PGCert, PGDip

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Organisation
King’s College London
Start - End
8 Sep
Study Options
Full Time
Fee
GBP 14070 - 14070
Fee International
GBP 32940 - 32940

The Applied Statistical Modelling and Health Informatics course has been created to deliver a skill set and knowledge base in “multimodal” and “big data” analysis techniques, which are a recognised scarcity within UK Life sciences.

You will receive world-class training in core applied statistical methodology, machine learning and computational methodology, and you will have the opportunity to apply your skills to real-life settings facilitated by the world-leading Institute of Psychiatry, Psychology & Neuroscience.

The course will apply to a broad spectrum of graduates preparing for a career in medical statistics and health informatics, or professional methodologists and clinical researchers working in the private or public health sector who are interested in state-of-the-art technologies training from world-class experts.

This course is also suitable if you are a graduate in/work in the fields of computer science, maths, physics, engineering and natural science, including psychology and medicine.

The course will prepare participants for the ever-growing need for a sound scientific approach to processing information and generating knowledge in modern health services. Practical skills will be taught through applications to real-life settings in a world-leading research institution in mental health

Key benefits

  • Specialist careers for those interested in applied statistical modelling and health informatics within a modern information and knowledge-based medical research setting.
  • Valuable insight from those working at the cutting edge of these fields.
  • A unique flexibly structured programme ideal for researchers in industry and academia hoping to obtain methodological skills to boost their research excellence and employability.
  • Develop your skills in multimodal and big data analysis techniques.
  • Learn how omics, electronic health records, social media, wearables and smartphone data are used to benefit the patient.
  • Apply your skills to real-life health settings facilitated by the world-leading Institute of Psychiatry, Psychology & Neuroscience.
  • Access the IoPPN’s statistical and health informatics research groups and benefit from our links with industry and the NHS.
  • Benefit from outstanding career, networking and mentoring opportunities

Structure

Courses are divided into modules. Each year you will normally take modules totalling 180 credits for the MSc, 120 credits for the PG Dip and 60 credits for the PgCert. The course offers a unique delivery to allow flexibility of learning. Each programme module runs over 6-weeks and is made up of a familiarisation week supported through our VLE, 5 days on campus, face-to-face teaching and 4 weeks for independent (self) learning and assessment. The exam will take place on campus and tutors and module leads will communicate with you throughout the programme using our VLE

Required Modules

You are required to take:

MSc

  • Introduction to Statistical Modelling (15 credits)
  • Introduction to Statistical Programming (15 credits)
  • ASMHI Research Project (60 Credits/For MSc only)

PG Dip

  • Introduction to Statistical Modelling (15 credits)
  • Introduction to Statistical Programming (15 credits)

PG Cert

  • Introduction to Statistical Modelling (15 credits)
  • Introduction to Statistical Programming (15 credits)

(Exceptions to Introduction to Programming would be made for students who can show they have significant programming experience; students would then take three modules from the list above)

Optional Modules

Students will take two/six modules from a range of optional modules for this course depending on whether they are taking the PGCert or PGDip/MSc are:

  • Introduction to Health Informatics (15 credits)
  • Multilevel and Longitudinal Modelling (15 credits)
  • Prediction Modelling (15 credits)
  • Causal Modelling and Evaluation (15 credits)
  • Machine Learning for Health and Bioinformatics (15 credits)
  • Clinical trials: A practical approach (15 credits)
  • Natural Language Processing (NLP) (15 credits)
  • Contemporary Psychometrics (15 credits)
  • Introduction to Computational Neuroscience (15 credits)
  • Structural Equation Modelling (SEM) (15 credits)
  • Artificial Intelligence in Health Analytics (15 Credits)
  • Bioinformatics, Interpretation and Data Quality in Genome Analysis (15 Credits)
  • Advanced Bioinformatics: Practical Bioinformatics Data Skills (15 Credits)

King’s College London reviews the modules offered on a regular basis to provide up-to-date, innovative and relevant programmes of study. Therefore, modules offered may change. We suggest you keep an eye on the course finder on our website for updates.

Career prospects

There is an increasing demand for individuals who can understand and manage information and information systems as well as analyse and interpret complex data in industry, public health sector and academia.

This PGDip programme offers a unique blend of training in applied statistics and health informatics science to enable graduates for roles in this rapidly emerging field of data-intensive medical research. Upon completion, you will have a solid understanding of the methodologies and application that are relevant to design and implement clinical research studies that take advantage of the increasing availability of multimodal and big data. This will include health data acquisition, storage, interoperability, data analyses and interpretation and communication of findings. Postgraduate qualifications are often required to progress to senior positions.

Our course will help you to develop your career in the medical research and health sector, including public health service (NHS), Pharmaceutical and computer companies, technology start-ups, government agencies as well as academia and other scientific organizations. Knowledge of applied statistical modelling and data science will also open careers in other sectors such as banking, marketing, insurance and consulting. It will also provide a strong foundation for students interested in obtaining a PhD in biostatistics, data science or informatics with an emphasis on applications in health science.


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