Applied Statistical Modelling & Health Informatics

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.

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

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

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.

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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.