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Applied Statistical Modelling & Health Informatics PGCert

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New Postgraduate Scholarships Available at the IoPPN for 2019 Entry

This 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 statistical, 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.

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 information sheet is a printable version of the information on this web page, which you can download here.


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.

 

Key information

Application status Open

Duration One Year

Study mode Full-time

Credit value UK 60/ECTS 30

Course leaders The course leader is Professor Daniel Stahl and Zahra Abdulla (Senior Teaching Fellow)

Further details

Course contact for further information

Please contact either Zahra.Abdulla@kcl.ac.uk or the general enquiry email for the course [kcl-asmhienquiries@kcl.ac.uk] for more information

Course contact form Postgraduate admissions

Awarding institution King's College London

Faculty Institute of Psychiatry, Psychology & Neuroscience

Department Biostatistics Department

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Course detail

Description

Our course is to meet the growing need for a graduate training course that focusses on methodological skills to respond to problems of “big data” of complexes diseases, which is underpinned by strong statistical methodology and real-world application.”

There is an increasing demand for the acquisition, storage, retrieval and use of information within private and public sector institutions engaged in health research. The range of modern medical data is vast, from patient records, genetics, other omics and imaging data, to real-time measures of physiological responses from wearable sensors, smartphone social media use and environmental data. We will provide you with the necessary state-of-the-art statistical modelling and health informatics techniques to manage and evaluate this data.

You will receive training in key methodological techniques underpinning “big data” acquisition, information retrieval and analysis using prediction modelling and theory driven analyses approaches.

You will benefit from the teaching of world-renowned experts in the field, you will conduct an applied research project and link to statistical and health informatics research groups, such as the causal modelling group, precision medicine and statistical learning, measurement theory, health informatics and natural language processing groups in the Department of Biostatistics and Health Informatics.

You will be part of a multi-professional cohort, bringing together diverse points of view on national and international modern data dilemmas.  You will also have the unique opportunity to network and develop career opportunities.

Our course combines training in core statistical, machine learning and computational methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical modelling and health informatics. Each year you will normally take modules totalling 60 credits for the PGCert.

The course offers a unique delivery using a blended distance learning approach to allow flexibility of learning. Each programme module runs over 6-weeks and is made up of an off campus (online distance learning) familiarisation week, 5 days on campus, face-to-face teaching and 4 weeks off campus online distance learning.

 

 

Course format and assessment

You will be taught through a mix of lectures, seminars and tutorials. 

Format

Introduction to Statistical Modelling

Lectures (20 hours) | Seminars/Tutorials (15 hours) | Self-Study time (115 hours) 

Introduction to Statistical Programming

Lectures (15 hours) | Seminars/Tutorials (15 hours) | Self-Study time (120 hours)

Introduction to Health Informatics

Lectures (20 hours) | Seminars/Tutorials (10 hours) | Self-Study time (120 hours)

Multilevel and Longitudinal Modelling 

Lectures (15 hours) | Seminars/Tutorials (15 hours)  Self-Study time (120 hours)

Prediction Modelling  

Lectures (15 hours) | Seminars/Tutorials (25 hours) | Self-Study time (110 hours)

Causal Modelling and Evaluation

Lectures (15 hours) | Seminars/Tutorials (20 hours) | Self-Study time (115 hours)

Machine Learning for Health and Bioinformatics 

Lectures (15 hours) | Seminars/Tutorials (20 hours) | Self-Study time (115 hours)

Clinical trials: A practical approach

Lectures (20 hours) | Seminars/Tutorials (20 hours) |  Self-Study time (110 hours)

Natural Language Processing (NLP)

Lectures (15 hours) | Seminars/Tutorials (20 hours) | Self-Study time (115 hours)

Contemporary Psychometrics

Lectures (15 hours) | Seminars/Tutorials (20 hours)  Self-Study time (115 hours)

Structural Equation Modelling (SEM)

Lectures (15 hours) | Seminars/Tutorials (15 hours)  Self-Study time (120 hours)

Introduction to Computational Neuroscience

Lectures (15 hours) | Seminars/Tutorials (20 hours) | Self-Study time (100 hours)

 

Our course combines training in core statistical, machine learning and computational methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical modelling and health informatics. The course offers a unique delivery using a blended distance learning approach to allow flexibility of learning. Each programme module runs over 6-weeks and is made up of a, off campus online distance learning familiarisation week, 5 days, on campus, face-to-face teaching, and 4 weeks off campus online distance learning.

Typically, one credit equates to 10 hours work

Assessment 

You are assessed through a combination of summative assessments which count towards your final module mark and formative assessments which are used to support your learning and development through the course.  You may typically expect summative assessment by:

PG Cert | Examination (30%) | Coursework (70%) 

You will also develop an e-portfolio of competencies and skills you have gained through the course to support your employability and development.  The study time and assessment methods detailed above are typical and give you a good indication of what to expect.  However, they may change if the course modules change.


Regulating body

Kings College London is regulated by the Office for Students.

Structure

Year 1

Courses are divided into modules.  Each year you will normally take modules totalling 60 credits for the PgCert.  The course offers a unique delivery using a blended distance learning approach to allow flexibility of learning, with each programme module running over 6-weeks which is made up of an off campus (online distance learning) familiarisation week, 5 days on campus, face-to-face teaching, and 4 weeks off campus online distance learning.

You are required to take: 

  • Introduction to Statistical Programming (15 credits) 

  • Introduction to Statistical Modelling (15 credits) 

Optional modules: 

In addition, students take two modules from a range of optional modules that may typically include:  

  • 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) 

  • Structural Equation Modelling (SEM)��(15 credits) 

  • Introduction to Computational Neuroscience (15 credits) 

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

Kings 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.

Required Modules
Optional Modules

Entry requirements & how to apply

Entry requirements
Minimum requirements 2:1

A bachelor’s degree with 2:1 honours in Computer Science, Mathematics, Statistics, Physics, Natural Sciences, Electronic Engineering, Psychology or Geographic Information Systems. In order to meet the academic entry requirements for this programme you should have a minimum 2:1 undergraduate degree with a final mark of at least 60% or above in the UK marking scheme. If you are still studying you should be achieving an average of at least 60% or above in the UK marking scheme.

Candidates who achieved a 2:2 in their undergraduate degree will need to support their application with a one page personal statement and an academic reference addressing their academic and relevant professional achievement.

Other degrees or professional qualifications may also be acceptable such as the Graduate diploma of the Royal Statistical Society.

International requirements   Visit our admissions webpages to view our International entry requirements.
English Language requirements Band D Visit our admissions webpages to view our English language entry requirements.

 

Application procedure

Applications must be made online using King’s online application portal apply.kcl.ac.uk and a non-refundable application fee of £60 applies.

Selection is made on the basis of application and references. Potential students are welcome to visit the department: please arrange a suitable time in advance.

Personal statement and supporting information

You will be asked to submit the following documents in order for your application to be considered:

Document checklist
Personal Statement Yes A personal statement of up to 4,000 characters (maximum 2 pages) is required
Previous Academic Study Yes A copy (or copies) of your official academic transcript(s), showing the subjects studied and marks obtained. If you have already completed your degree, copies of your official degree certificate will also be required. Applicants with academic documents issued in a language other than English, will need to submit both the original and official translation of their documents.
References No References are not required as part of an application
Other Optional You may wish to include a CV (Resume) or evidence of professional registration as part of your application.

Application closing date

We recommend that you submit your application as soon as possible.  Our first application deadline is the 29 March 2019.  Applications will remain open if places are available and programmes will be closed as soon as they are full.

Help and support

If you don't have a suitable qualification for direct entry to a UK university, or if English isn't your first language, our academic preparation courses can help you get ready for study in the UK.

Preparation courses

Fees and funding

  • Full time Home/EU fees: £4,100 per year (2019/20)
  • Full time overseas fees: £9,500 per year (2019/20)
  • Part time Home/EU fees: N/A
  • Part time overseas fees: N/A

Students starting their programme in 2019/20 who are eligible to pay EU fees will pay the same rate of tuition fees as UK students. This will apply for the duration of their programme, but may be subject to change by the UK Government for subsequent cohorts from 2020/21.

These tuition fees may be subject to additional increases in subsequent years of study, in line with King’s terms and conditions.

Deposit

When you receive an offer for this course you will be required to pay a non-refundable deposit to secure your place. The deposit will be credited towards your total fee payment.

The UK/EU deposit is £500.

The INTERNATIONAL deposit is £2,000. 

  • If you receive an offer on or before 31st March 2019, payment is due by 27th April 2019.
  • If you receive an offer between 1st April 2019 and 30th June 2019, payment is due within one month of receiving the offer.
  • If you receive an offer between 1st July 2019 and 31st July 2019, payment is due within two weeks of receiving the offer.
  • If you receive an offer on or after 1st August 2019, payment is due within one week of receiving the offer.

 

For further information, please visit the fees and funding section of our website:  www.kcl.ac.uk/study/postgraduate/fees-and-funding/index.aspx 

Additional Costs 

In addition to your tuition fees you can also expect to pay for: 

  • Books if you choose to buy your own copies 

  • Library fees and fines 

  • Personal photocopies 

  • Printing course handouts 

  • Society membership fees 

  • Stationery 

  • Travel costs to London for face-to face sessions and for travel around London. 

  • Graduation costs 

For further information, please visit the fees and funding section of our website: 

www.kcl.ac.uk/study/postgraduate/fees-and-funding/index.aspx 

Disclaimer 

Although this PDF was up-to-date at the time it was produced, please make sure you check our website www.kcl.ac.uk/study or contact us directly for the very latest information before you commit yourself to any of our courses. 

Financial help and support

Visit the fees and funding webpages to find out more about bursaries, scholarships, grants, tuition fees, living expenses, student loans and other financial help available at King's.

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, the public health sector and academia.

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.

Knowledge of applied statistical modelling and data science will open up careers across sectors. This is the ideal next step if you’re looking to work/already work in industry, commerce, health or academia. The course will also provide a strong foundation for students interested in obtaining a PhD.

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Next steps

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