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PU5050: OPEN AND REPRODUCIBLE HEALTH DATA SCIENCE (2021-2022)

Last modified: 16 Aug 2021 13:56


Course Overview

The way we do science is changing. Scientific results that can be independently verified increase trust in science and accelerate future work.

 

This course will give students the tools they need to do open and reproducible health data science. The skills they will develop are becoming a requirement for funding agencies and scientific publishers, and are important for data-intensive careers in academia, NHS or industry.

Course Details

Study Type Postgraduate Level 5
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr Dimitra Blana

What courses & programmes must have been taken before this course?

  • Any Postgraduate Programme

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

Are there a limited number of places available?

No

Course Description

The aim of the course is to enable students to carry out open and reproducible health data science. The course will cover

principles of open science; advantages and barriers to reproducibility; health data management; reproducibility initiatives such as registered reports and preprints; version control; collaboration using GitHub; code development using R (no coding experience is required).


In light of Covid-19 this information is indicative and may be subject to change.

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

  • 1 Computer Practical during University weeks 9 - 19
  • 1 Seminar during University weeks 9 - 19

More Information about Week Numbers


In light of Covid-19 and the move to blended learning delivery the assessment information advertised for second half-session courses may be subject to change. All updates for second-half session courses will be actioned in advance of the second half-session teaching starting. Please check back regularly for updates.

Summative Assessments

Report: Individual

Assessment Type Summative Weighting 70
Assessment Weeks 12 Feedback Weeks 15

Look up Week Numbers

Feedback

A homework assignment covering all aspects of a reproducible data science workflow

Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualAnalyseDiscuss the advantages of Open and Reproducible Health Data Science and the barriers to its adoption
ConceptualUnderstandExplain how openness can be embedded in the scientific process
ProceduralApplyUse the R programming language to import, analyse and visualise health data

Poster Presentation

Assessment Type Summative Weighting 30
Assessment Weeks 11 Feedback Weeks 14

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Feedback

Critique of published study focusing on openness and reproducibility, with suggestions for improvement

Learning Outcomes
Knowledge LevelThinking SkillOutcome
ProceduralAnalyseEmbed reproducibility principles into the life cycle of health data
ProceduralCreateDesign a reproducible health data science workflow

Formative Assessment

There are no assessments for this course.

Resit Assessments

Report: Individual

Assessment Type Summative Weighting 100
Assessment Weeks 40 Feedback Weeks 43

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Feedback

A report describing a reproducible data science workflow from initial dataset to visualisation of results, including code (in R

Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ConceptualUnderstandExplain how openness can be embedded in the scientific process
ProceduralApplyUse the R programming language to import, analyse and visualise health data
ConceptualAnalyseDiscuss the advantages of Open and Reproducible Health Data Science and the barriers to its adoption
ProceduralAnalyseEmbed reproducibility principles into the life cycle of health data
ProceduralCreateDesign a reproducible health data science workflow

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