Health Data Science (MSc)

Stage 1: Compulsory Courses

Applied Statistics (PU5017)

15 Credit Points

This course in Applied Statistics intends to focus on the application of statistical techniques in postgraduate research for health professionals, with a particular emphasis on the correct interpretation of statistical analyses. The course will NOT concentrate on the statistical theory underlying the subject. An important component of the course is the use of a statistical package, IBM SPSS Statistics, which can be used to implement all the methods taught on the course.

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Getting Started at the University of Aberdeen (PD5006)

This course, which is prescribed for all taught postgraduate students, is studied entirely online, takes approximately 2-3 hours to complete and can be taken in one sitting, or spread across the first 4 weeks of term.

Topics include University orientation overview, equality & diversity, MySkills, health, safety and cyber security, and academic integrity.

Successful completion of this course will be recorded on your Transcript as ‘Achieved’.

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Introduction to Health Data Science (PU5063)

15 Credit Points

Nationally and internationally there is recognition of the critical shortage in data-intensive analytic capacity applied to healthcare. This course is an introduction to the field of health data science, with examples of real-life healthcare applications, using the popular data science language R.

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Stage 1: Optional Courses

Students select TWO courses from the following:

Epidemiology (PU5030)

15 Credit Points

This course in applied epidemiology gives an introduction to disease measurement at a population level, basic epidemiological study design and analysis, and provides an understanding of key methodological issues needed to apply when designing – or critically appraising – an epidemiological study.

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Database Systems and Big Data (CS5097)

15 Credit Points

This course will be of interest to anyone who wishes to learn to design and query databases. The course aims to teach the material using case studies from real-world applications. You will develop a critical understanding of the principal theories, principles and concepts, such as modelling techniques used in the design, administration and security of database systems. You will also learn core theoretical concepts such as relational algebra, file organisation and indexing. At the end of this course you will be able to design and build Web and cloud-based databases and have a critical understanding of how database-driven applications operate.

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Fundamentals of Research Design (PU5052)

15 Credit Points

This course introduces you to health research methods, focusing on designing strong research proposals. You'll learn to formulate research questions, choose study designs, identify outcomes, and plan data collection.

We will explore key study designs, from experimental to observational, and master sampling and data collection for both qualitative and quantitative research. You'll also develop skills in critical appraisal and research ethics, equipping you to design rigorous and impactful research.

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Designing Real - World Trials (PU5068)

15 Credit Points

This course will focus on trials in the evaluation of real-world healthcare and public health settings. The course is run by staff from our world-leading Centre for Healthcare Randomised Trials (CHaRT) and the Aberdeen Centre for Evaluation - awarded the Queen's Anniversary Award for sustained excellence in health services research. Through studying this course, you will develop the knowledge and awareness of how to design a fair test, the appropriate use of trials and alternative trial designs, involving patients and the public, and sample size considerations.

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Stage 2: Compulsory Courses

Students must take the following courses:

Health Informatics (PU5565)

15 Credit Points

We live in a time of ‘Big Data’ with the rapid growth in the digital capture of health information. Health Informatics is the science of data capture, linkage and analysis of large datasets to improve health. The demand for health researchers with training and experience in health informatics is high. For people practicing in Public Health, it is a key skill. It will equip students for any career in health research or public health practice and this course is an excellent stepping stone for those wishing to develop a specialist interest in the field.

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Machine Learning for Healthcare (PU5567)

15 Credit Points

Machine learning has the potential to revolutionise healthcare. The aim of this course is to introduce machine learning for health data science with examples of real-life healthcare applications, using the popular data science language R.

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Stage 2: Optional Courses

Students must select TWO courses from the following:

• PU5576: Systematic Reviews for Clinical Practice and Health Policy

Please note that PU5548: Work-based Placement in Applied Health Sciences can not be taken with courses PU5930 or PU5926.

Work - Based Placement in Applied Health Sciences (PU5548)

15 Credit Points

This work-based placement elective offers a professional placement with a government/public, industrial, civic or voluntary health and/or development sector organisation. You will undertake a ten-week placement with your host organisation, either within the organisation, remotely from Aberdeen, or using a combination of both. Placements are subject to availability and may be offered on a competitive basis. We reserve the right to remove a student from placement should an employer report any inappropriate behaviour or unacceptable work. Should this happen, University-based remedial training supervised by course staff will be offered as an alternative activity and assessments will remain in place but will be capped at D3.

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Health Systems and Policy Research (BU5594)

15 Credit Points

The course aims to provide foundational knowledge while placing emphasis on fostering critical thinking about the key challenges confronting health systems and the strategies to enhance health. With a global perspective, it examines diverse healthcare systems, encouraging students to compare, analyse, and critique them. Topics include the impact of social inequalities on health disparities and their implications for social policy, the use of charges for health care and their impact on health care use and health as well as public health approaches and their connections to issues such as unemployment and obesity.

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Understanding and Applying Regression Models (PU5569)

15 Credit Points

This intermediate-level course intends to advance a student's statistical skills and understanding of common and more advanced regression modelling techniques so that they can apply them to a wide range of health research data. The course will focus on introducing the student to the concepts underpinning generalised linear models. They will deepen their understanding of linear and logistic regression and learn how to analyse outcomes such as count data and time-to-event data using regression for count data and survival analysis. This course will focus on the application, interpretation, and communication of the learned methodologies. It assumes that students will already have completed a first course in introductory statistics and have an understanding of hypothesis testing and basic mathematical skills.

We strongly recommend signing up for this course only if you have solid knowledge and experience of basic statistical concepts and methodologies used for descriptive statistics (e.g. mean, standard deviation and other measures on central tendency and dispersion) and statistical inference (e.g. standard error, confidence intervals, hypothesis tests such as t-test and ANOVA). Knowledge or experience of simple linear regression is preferable but not essential.

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Health Economics (PU5571)

15 Credit Points

Resources available for the provision and payment for health care are limited. However, knowledge of economics helps ensure that available resources are used in the most effective way possible. Economics allows more informed decision making about a variety of issues: choosing between alternative treatments; setting priorities between patients; choosing between alternative new technologies; organising the provision of health care.

In this course students will acquire a knowledge and understanding of:

• Key themes of economic theory applied to health care
• Some of the main techniques of health care evaluation
• Main arguments concerning alternative systems for organising and financing health care

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Data Science: from Data to Insight (CS5703)

15 Credit Points

Data Science is an interdisciplinary field that seeks to identify and understand phenomena captured in structured or unstructured data, extract insights, and add value by generating predictions that aid optimization of processes and equipment. These techniques show considerable promise for bringing about a revolution, increasing the significance and value of owning and collecting data of all types. This course introduces the common techniques and considers the implications for data managers.

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Stage 3: Optional Courses

Select either the 60 credit course or two 30 credit courses:

• Masters Research Project (PU5922) (60 credits)

OR

• Professional Placement in Health Data Science (PU5926) (60 credits)

OR

• Evaluating policy effects in practice (PU5925) (30 credits)
• Enhanced Work-Based Placement in Applied Health Sciences (PU59 ) (3030 credits)

Masters Research Project (PU5922)

60 Credit Points

This course offers students the opportunity to complete a substantial piece of data-driven, empirical work within their field of study under the supervision of an experienced researcher.

Topics available will be varied but within the domain of their field of study. Alongside supervisors, students will identify a suitable topic area, describe an appropriate study design and implement an empirical study. Students will be involved alongside the supervisors in the process of defining the research question, and developing the research plan and, where appropriate, obtaining regulatory approvals. This course is for non-laboratory based projects (if you are intending to undertake a project in a scientific laboratory setting, you should register on MB5904).

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Professional Placement in Health Data Science (PU5926)

60 Credit Points

This work-based placement elective offers a professional placement with a civic, government, industrial, public, research or voluntary health and/or development sector organisation in the field of Health Data Science. You will undertake a ten-week placement with your host organisation, either within the organisation, remotely from Aberdeen, or using a combination of both. Placements are subject to availability and are offered on a best match basis.

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