GD5301 Health Data Science Principles

Academic year

2024 to 2025 Semester 1

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

30

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Open to MSc Digital Health students only.

Planned timetable

To be arranged

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Prof S Paracchini

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Team taught; teaching staff confirmed at start of semester.

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

Health Data Science and digital technology are transforming healthcare by enabling faster diagnosis and better treatment of illnesses, supporting improvements in patient care, and making healthcare altogether more efficient. This module brings together academic staff and external partners from technical and clinical backgrounds to provide a learning experience that encompasses clinical problems and the distinctive solutions that Health Data Science provides. You will learn about the theoretical underpinnings of Health Data Science, its different forms, the digital technology and methods it employs, and how digital data is integrated in clinical decision-making. You will examine how interdisciplinarity is helping advance the work of Health Data Science across academia and other sectors. You will develop an appreciation of the ethical implications in handling, storing, and analysing big data. You will develop practical skills in explaining Health Data Science to different audiences.

Assessment pattern

Coursework = 100% - (50% report, 25% poster with audio, 25% podcast with visual aid).

Re-assessment

Coursework = 100%

Learning and teaching methods and delivery

Weekly contact

2 x 1 hour weekly lectures, plus additional skills workshops in some weeks

Scheduled learning hours

30

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

264

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Be familiar with different types of health data, the technology that generate them, methods used for processing and analysis, and how digital data is integrated in clinical decision making.
  • Understand the challenges in handling, storing and analysing big data as well as the ethical implications relevant to the governance of patient data.
  • Understand the nature of interdisciplinary work across academia and other sectors that is required to advance digital health, and health data science and appreciate the challenges associated with it.
  • Become confident in presenting and discussing health data science and digital health topics through different media and to summarise technical content effectively for different audiences.