Health Data Science MSc

Dubai Campus

Start date
September 2024, January 2025
Duration
18 months part-time (Jan 2024), 1 year full-time (Sep 2024), 2 years part-time (Sep 2024)
Campus
Dubai
Course Type
Postgraduate, Taught
Fees

Annual tuition fee for 2024/25:
AED 138,456 (full-time)

Scholarships available for all students up to 40%

 

This Masters programme takes you into the fascinating world of cutting-edge technology, health data, and the limitless potential of artificial intelligence. The programme is designed to prepare you for a career in industry or academia at the intersection of AI, clinical sciences, and biomedicine.

Subject to Ministry of Education accreditation

Health data science is transforming the healthcare landscape by harnessing the power of data to improve patient outcomes and optimize medical procedures. It enables evidence-based decision-making, empowers healthcare professionals, and contributes to the development of innovative treatments and personalized medicine.

The demand for health data scientists is experiencing exponential growth, driven by the ever-increasing volume and complexity of health data. In recent years, we have transitioned from facing a shortage of data to having shortage in experts who can effectively analyse this wealth of information.

In this dynamic programme, we'll equip you with the expertise and tools needed to unravel the potential of health data and how it can transform medicine. You will learn how to use advanced computational techniques to unlock new frontiers in clinical and biomedical research and be at the forefront of innovation in this rapidly evolving field.

Our students come from diverse backgrounds from the biomedical and medical domains, including clinical trainees, as well as individuals with expertise in computer science, mathematics, and statistics. Additionally, we welcome students from public health, epidemiology, and biotechnology/engineering disciplines, fostering a rich and multidisciplinary learning environment.

Delivery

This course is delivered in the evenings and on weekends.

Programme Director

Dr Marc Haber

Marc Haber profile picture

Dr Marc Haber is an Associate Professor at the Institute of Cancer and Genomic Sciences and leads the Health Data Science Programme at the Dubai Campus. His group investigates genetic variation in humans to explain differences between populations in disease incidence and progression. The group focuses on genetically underrepresented populations to understand how their genomic history has contributed to their disease burden.

Marc’s background is in population genetics. He uses large-scale sequencing data to learn about the events that shaped the human genome; this involves investigating how humans migrated and spread around the world, admixed, and adapted to new environments and the consequence of these events on human traits and diseases.

View Dr Haber's staff profile. 

Programme Deputy Director

Dr Animesh Acharjee

Dr Animesh Acharjee

Dr Animesh Acharjee is an Assistant Professor of Integrative Analytics and AI and Deputy Programme Director, MSc in Health Data Science (Dubai) at the Institute of Cancer and Genomic Sciences. Animesh’s background is in biology and statistical machine learning. His group specializes in integrative analytics, using machine learning/AI techniques to combine diverse multi-omics data. He is also interested in diagnostics and is actively involved in developing bioinformatics tools and workflows for clinicians and biologists.

Dr Animesh Acharjee is an Assistant Professor of Integrative Analytics and AI and Deputy Programme Director, MSc in Health Data Science (Dubai) at the Institute of Cancer and Genomic Sciences. Animesh’s background is in biology and statistical machine learning. His group specializes in integrative analytics, using machine learning/AI techniques to combine diverse multi-omics data. He is also interested in diagnostics and is actively involved in developing bioinformatics tools and workflows for clinicians and biologists.

View Dr Acharjee's staff profile

I would recommend this course to anyone who wants to embrace and start their journey into data science.

MSc Health Data Science student

Why study this course?

The MSc Health Data Science programme offers you the opportunity to:

  • Learn about the dynamic, collaborative, and interdisciplinary healthcare ecosystem that spans academia and industry.
  • Attain proficiency in computational techniques for health data analysis, even if you have no prior computational background.
  • Develop a profound comprehension of healthcare systems, their ethical underpinnings, and the intricacies of governance. Learn about the present and prospects of health data science and its role in personalised medicine.
  • Recognize the transformative potential of health data science skills in reshaping healthcare data and unlocking the power of patient-specific information including genomics. These capabilities are driving advancements in research, clinical care, and fostering innovation in the 21st century.
  • Grasp the significance of diverse health information sources, systems, integration methods, and the role of information technologies in healthcare delivery.
  • Acquire hands-on experience working in diverse, multi-disciplinary healthcare environments and related research.
  • Enhance your communication and delivery skills within the broader healthcare landscape.

Modules

Please note: The modules listed on the website for this programme are regularly reviewed to ensure they are up-to-date and informed by the latest research and teaching methods.

  • Foundations of Computing Practices in Health Data Science (20 credits)

This module covers the fundamentals of health data management, extraction, and manipulation using Python programming. It also introduces students to data visualisation techniques for health data analytics.

  • Essentials of Mathematics and Statistics (20 credits)

This module provides an introduction to essential quantitative theory in health data science. It covers concepts through a series of core problems, which will be explored in more detail in later modules. The quantitative topics include Probability Theory, Descriptive Statistics, Hypothesis Testing, and an introduction to Statistical Modeling using the R programming language, including linear models and estimation.

  • Data Analytics & Statistical Machine Learning (20 credits)

The aim of this module is to provide a comprehensive understanding of the current advancements in data integration, mining, and analysis, with a focus on applications in health data science and biomedicine. The topics covered include various aspects of data, such as data types, data modelling, data management, integration techniques, as well as supervised and unsupervised machine learning models and validation approaches.

  • Health Data Fundamentals (20 credits)

The module introduces key concepts in various multi-modal health data types and modalities and provides an overview of how health data science can revolutionise healthcare data use. It covers data governance, ethical implications, patient and public involvement, and informed consent. Additionally, it introduces the fundamentals of various -omics and genetics fields and their role in revealing disease pathobiology and implications in personalised medicine.

  • Epidemiology and Health Informatics (20 credits)

This module introduces key concepts in epidemiology and health information at both the population and individual levels. It covers topics such as descriptive epidemiology, measures and comparisons of disease occurrence (incidence, prevalence), and various study designs in epidemiology, including ecological studies, cross-sectional studies, case-control studies, cohort studies, and randomised controlled trials. The course also provides an introduction to fundamental epidemiological concepts used to identify and quantify disease and associated hazards.

  • Integrative Multimodal Data Analytics (20 credits)

This module builds upon previous modules and covers advanced topics in health data science. It introduces image analysis, electronic health records data, longitudinal modelling, and integration with multi-omics datasets. The module also explores advanced modelling methods, including deep neural networks and omics fusion strategies.

  • Interdisciplinary Health Data Research Project (60 credits)

The dissertation module offers students the opportunity to demonstrate their acquired knowledge and skills from the taught modules. Dissertations must include a computational work in the health data field, and students are encouraged to select their own topics with the guidance of a supervisor. Successful dissertations delve deeply into a health data subject, posing clear research questions, employing suitable methodology, and critically analysing the results.

Fees

We charge an annual tuition fee for 2024 entry:

  • Full-time: AED 138,456
  • Part-time: AED 69,228

Scholarships

We have a number of scholarships available.

Scholarship information

How To Apply

To find out more about our application process please visit our how to apply page before starting your application.

 

Our Standard Requirements

A 2:1 undergraduate honours degree in a medical/life science (medicine, biology, chemistry, etc.) or quantitative science subject (computer science, mathematics, physics, etc.). Equivalent relevant work experience will also be considered for eligibility to enter the programme.

International students

Academic requirements

We accept a range of international qualifications - use the dropdown box below to select your country and see the equivalencies to the above UK requirements.

English language requirements

IELTS 6.5 with no less than 6.0 in any band.

If you are made an offer of a place to study and you do not already meet the language requirement, you have the option to enrol on our English for Academic Purposes Presessional Course. If you successfully complete this, you will be able to fulfil the language requirement without needing to take a further qualification.

International Requirements


Course delivery

We offer a combination of evening and weekend classes which are designed to accommodate working professionals and those who choose to study full-time.

Our draft timetable is designed to deliver teaching on two or three evenings from 18:00-21:00 and on Saturday and Sunday from 10:00-17:00. Each module is block taught over 2 weeks. Part-time students enrol in the same classes as their full-time counterparts; however, they take fewer number of modules each year.

Please note that the example timetable is to be used as a guide and is subject to change. Our term dates are also available online. For further information, please contact the Programme Director, Dr Marc Haber m.haber@bham.ac.uk.

View example timetable for MSc Health Data Science (PDF 95.2 KB)

Assessment Methods

You will be assessed through a variety of methods, including essays, exams, oral presentations, computer-based problem solving exercises and a thesis.

This programme will give you clear and compelling experience of working across academic, healthcare and industry sectors, with extensive supervisory and mentoring arrangements to maximise your exposure to these environments. Not only will this prepare you mentally and practically, it will also help identify specific opportunities and contacts for progress into relevant career pathways.

Careers Service

During your studies you will have access to a wide range of dedicated support, helping you to achieve your career goals. These include:

  • Individual careers advice from the International Careers Consultant and specialist careers advisers, who will be able to answer your requests for advice and guidance
  • Apply Yourself coaching workshops and online tutorials to help you present yourself effectively to employers and recruiters and develop an effective CV
  • Practical help with preparing for interviews and attending assessment centres Access to global job opportunities online and job search webinars
  • Opportunities to network with alumni and potential employers
  • Access to a range of careers information online to help you consider your next stage after your degree and develop an individual career action plan

By making the most of the wide range of services available, you will be able to develop your career from the moment you arrive.