MSc Health Data Science

Year of entry: 2021

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Course unit details:
Tutorials in Health Data Science

Unit code IIDS67611
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by Division of Informatics, Imaging and Data Sciences
Available as a free choice unit? No

Overview

A health data scientist brings together individual pieces of knowledge and understanding in order to be able to devise an appropriate strategy to improve healthcare.  A key characteristic for this role is to be an adaptable, curious’ and entrepreneurial thinker who can synthesis information quickly to devise research strategies to improve healthcare delivery from individual treatments, service delivery to population health.  In order to do this it is key to be able to have an understanding of the wider academic research landscape and how individual health data science research fits within this.  This ‘translational’ thinking will drive new innovations and research within the health sector.  This unit will provide an environment from which student will confidently develop critical skills around a set of core academic papers.

Aims

This unit aims to

  • To demonstrate the range of health data science skills and research
  • To develop critical appraisal skills of original research papers and wider literature
  • To provide students with an environment in which to be able to learn how to conduct a research strategy
  • To develop core literature to illustrate key understanding of (health) data science skills and how this related to other areas/disciplines.
  • Equip students with ‘translational’ thinking skills to be able to work at the intersection of disciplines.

Learning outcomes

  • Develop an overview of the variety of research in health data science and how it fits in the wider research landscape.
  • Describe how methodologies, technologies and research can improve healthcare delivery.
  • Design a strategy for in-depth investigation of applicaiton examples
  • Critically appraise and assess published research papers in the field of health data science.
  • Effectively present concepts and resulting research strategy
  • Transfer knowledge and understanding between use cases and link concepts from individual elements of health data science to other fields/disciplines.

Teaching and learning methods

This unit is designed to be a student-led investigation around published academic literature.  

In small groups students will explore two related academic papers that use data science skills, but is not necessarily central to healthcare (one that is core to the field, and second that is cutting-edge) and devise and employ a strategy to be able to explore the area in more detail, and translate these findings to other research areas.  Key papers outlining case-studies will be made available online before the face-to-face workshops and online collaborative working tools will be made available to students in order to facilitate their work.  F2F time will consist of an opening lecture (1 hour per case-study) to introduce the key papers by academic members of staff; and a series of 5 x 3 hour supervised workshops to facilitate students through their investigation.  Each workshop students will report back on their progress; highlighted any issues or difficulties and develop ideas. 

Knowledge and understanding

  • Develop an overview of the variety of research in health data science and how it fits in the wider research landscape
  • Describe how methodologies, technologies and research can improve healthcare delivery

Intellectual skills

  • Design a strategy for in-depth investigation of application examples
  • Critically appraise and assess published research papers in the field of health data science

Practical skills

Effectively present concepts and resulting research strategy

Transferable skills and personal qualities

  • Work collaboratively in a group
  • Communicate their findings clearly in a written report and presentations
  • Transfer knowledge and understanding between use cases and link concepts from individual elements of health data science to other fields/disciplines

Employability skills

Group/team working
Work collaboratively in a group
Written communication
communicate findings clearly in written reports and presentations

Assessment methods

Method Weight
Report 70%
Oral assessment/presentation 30%

Feedback methods

Formative assessment and feedback to students is a key feature of the unit and will be provided in the face-to-face workshops.

Study hours

Scheduled activity hours
Lectures 4
Practical classes & workshops 30
Independent study hours
Independent study 116

Teaching staff

Staff member Role
Niels Peek Unit coordinator
Tjeerd Van Staa Unit coordinator

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