Master Global Public Health Nutrition - Máster en Nutrición y Salud Pública Global
Healthcare, with an already established strong relationship with Information & Communication Technologies (ICT), is continuously expanding the knowledge forefront as new methods of acquiring data concerning the health of human beings are developed.
Processing this data to extract valuable information about a population (epidemiological applications) or the individual (personalised healthcare applications) is the work of health data scientists. Their work has the potential to improve quality of life on a large scale.
Swansea University is the first institution in the UK to offer this taught masters programme designed to develop the essential skills and knowledge required of the Health Data Scientist.
Admission to this course is normally on the basis of UK Honours Degree Grade 2:2 or above or an equivalent qualification for overseas applicants.
Non-graduates are also welcome to apply. All applications are considered on individual merit, taking into account of any relevant work experience. Should you have qualifications below the required minimum or lack a suitable first degree, please feel encouraged to submit an application if you have at least two years of experience in Health and or Data Science related fields.
Applicants who are not first-language English speakers must provide one of the following qualifications:
IELTS Academic: 6.5 (minimum of 6.0 in each part)
TOEFL IBT: 93 (20 in each part)
Pearson PTE Academic: 62 (51 in communicative skill)
Cambridge CPE: Grade C
Sijil Pelajaran Malaysia: Grade 6 or above
Students must complete 6 modules of 20 credits each and produce a 60 credits dissertation on a Health Data Science project. Each module of the programme requires a short period of attendance that is augmented by preparatory and reflective material supplied via the course website before and after attendance.
Students are required to attend the University for 1 week (5 consecutive days) for each module in Part One. Attendance during Part Two is negotiated with the supervisor.
PMIM102 Health Data Science & Scientific Computing in Healthcare
PMIM202 Health Data Manipulation
PMIM302 Analysis of Linked Health Data
PMIM402 Machine Learning Applications in Health Data
PMIM502 Health Data Visualisation
PMIM602 Advanced Analysis of Linked Health Data
PMIM702 Health Data Analysis Dissertation