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Applied Statistical Modelling & Health Informatics MSc, PG Cert, PG Dip

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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

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 PG Cert.

Base campus

NEV-main-Denmark-Hill-Campus
Denmark Hill Campus

Home to the Institute of Psychiatry, Psychology & Neuroscience, the clinical and teaching facilities for the Faculty of Dentistry, Oral & Craniofacial Sciences and the Cicely Saunders Institute of Palliative Care, Policy & Rehabilitation.

Regulating bodies

King's is regulated by the Office for Students

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Teaching methods - what to expect

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 FT MSc year you will take:

  • 8 modules and Research Project totaling 180 credits for the MSc.

Each PT MSc year you will take:

  • 4 modules totaling 60 credits
  • 4 modules and Research Project totaling 120 credits.

The course has 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.

PG Cert and PGDip. Each year you will take:

  • 4 modules totalling 60 credits for the PG Cert;
  • 8 modules totalling 120 credits for the PGDip.

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.

We will use a delivery method that will ensure students have a rich, exciting experience from the start. Face to face teaching will be complemented and supported with innovative technology so that students also experience elements of digital learning and assessment.

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 (115 hours)

Artificial Intelligence in Health Analytics

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

ASMHI Research Project

Seminars/Tutorials (60 hours) | Self-Study time (540 hours)

Contact time is based on 36 academic weeks.

Typically, one credit equates to 10 hours of work.

 

Assessment

    The primary methods of assessment for this course are written examinations, coursework and an e-portfolio of competencies and skills, to support your employability and development. The study time and assessment methods typically give an indication of what to expect. However, these may vary depending upon the modules.

    For the full MSc each student will need to complete the ASMHI Research Project module, where assessment will be made up of a dissertation/research report and a poster/viva.

    Structure

    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)

    Optional modules

    Students will take two/six modules from a range of optional modules for this course depending on whether they are taking the PG Cert 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.

    Please note that modules with a practical component will be capped due to educational requirements, which may mean that we cannot guarantee a place to all students who elect to study this module.

    Employability

    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.

    Tuition Fees

    UK:

    Full time: £14,070 per year (MSc, 2022/23); £9,380 per year (PG Dip, 2022/23); £4,690 per year (PG Cert, 2022/23);

    Part time: £7,035 per year (MSc, 2022/23);

    International:

    Full time: £32,940 per year (MSc, 2022/23); £21,960 per year (PG Dip, 2022/23); £10,980 per year (PG Cert, 2022/23);

    Part time: £16,470 per year (MSc, 2022/23);

    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 deposit is £500.

    The INTERNATIONAL deposit is £2,000.

    • If you receive an offer on or before 31 March, payment is due by 25 April 2022.
    • If you receive an offer between 1 April and 30 June, payment is due within one month of receiving the offer.
    • If you receive an offer between 1 July and 31 July, payment is due within two weeks of receiving the offer.
    • If you receive an offer between 1 August and 21 August, payment is due within one week of receiving the offer.
    • If you receive an offer from 22 August onwards, payment is due within three days of receiving the offer.

    If you are a current King’s student in receipt of the King's Living Bursary you are not required to pay a deposit to secure your place on the programme. Please note, this will not change the total fees payable for your chosen programme.

    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 costs, you can also expect to pay for:

    • books if you choose to buy your own copies
    • laptop as one will be needed for face to face teaching
    • DBS checks
    • library fees and fines
    • society membership fees
    • stationery
    • travel costs for travel to and around London and between campuses/accommodation for your stay in London
    • graduation costs

    Funding

    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 PG Cert.

    Base campus

    NEV-main-Denmark-Hill-Campus
    Denmark Hill Campus

    Home to the Institute of Psychiatry, Psychology & Neuroscience, the clinical and teaching facilities for the Faculty of Dentistry, Oral & Craniofacial Sciences and the Cicely Saunders Institute of Palliative Care, Policy & Rehabilitation.

    Regulating bodies

    King's is regulated by the Office for Students

    Loading...

    Teaching methods - what to expect

    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 FT MSc year you will take:

    • 8 modules and Research Project totaling 180 credits for the MSc.

    Each PT MSc year you will take:

    • 4 modules totaling 60 credits
    • 4 modules and Research Project totaling 120 credits.

    The course has 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.

    PG Cert and PGDip. Each year you will take:

    • 4 modules totalling 60 credits for the PG Cert;
    • 8 modules totalling 120 credits for the PGDip.

    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.

    We will use a delivery method that will ensure students have a rich, exciting experience from the start. Face to face teaching will be complemented and supported with innovative technology so that students also experience elements of digital learning and assessment.

    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 (115 hours)

    Artificial Intelligence in Health Analytics

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

    ASMHI Research Project

    Seminars/Tutorials (60 hours) | Self-Study time (540 hours)

    Contact time is based on 36 academic weeks.

    Typically, one credit equates to 10 hours of work.

     

    Assessment

      The primary methods of assessment for this course are written examinations, coursework and an e-portfolio of competencies and skills, to support your employability and development. The study time and assessment methods typically give an indication of what to expect. However, these may vary depending upon the modules.

      For the full MSc each student will need to complete the ASMHI Research Project module, where assessment will be made up of a dissertation/research report and a poster/viva.

      Structure

      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)

      Optional modules

      Students will take two/six modules from a range of optional modules for this course depending on whether they are taking the PG Cert 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.

      Please note that modules with a practical component will be capped due to educational requirements, which may mean that we cannot guarantee a place to all students who elect to study this module.

      Employability

      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.

      Tuition Fees

      UK:

      Full time: £14,070 per year (MSc, 2022/23); £9,380 per year (PG Dip, 2022/23); £4,690 per year (PG Cert, 2022/23);

      Part time: £7,035 per year (MSc, 2022/23);

      International:

      Full time: £32,940 per year (MSc, 2022/23); £21,960 per year (PG Dip, 2022/23); £10,980 per year (PG Cert, 2022/23);

      Part time: £16,470 per year (MSc, 2022/23);

      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 deposit is £500.

      The INTERNATIONAL deposit is £2,000.

      • If you receive an offer on or before 31 March, payment is due by 25 April 2022.
      • If you receive an offer between 1 April and 30 June, payment is due within one month of receiving the offer.
      • If you receive an offer between 1 July and 31 July, payment is due within two weeks of receiving the offer.
      • If you receive an offer between 1 August and 21 August, payment is due within one week of receiving the offer.
      • If you receive an offer from 22 August onwards, payment is due within three days of receiving the offer.

      If you are a current King’s student in receipt of the King's Living Bursary you are not required to pay a deposit to secure your place on the programme. Please note, this will not change the total fees payable for your chosen programme.

      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 costs, you can also expect to pay for:

      • books if you choose to buy your own copies
      • laptop as one will be needed for face to face teaching
      • DBS checks
      • library fees and fines
      • society membership fees
      • stationery
      • travel costs for travel to and around London and between campuses/accommodation for your stay in London
      • graduation costs

      Funding

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