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Students
Tuition Fee
Start Date
2026-09-01
Medium of studying
On campus
Duration
1 years
Program Facts
Program Details
Degree
Masters
Major
Artificial Intelligence | Data Science | Biomedical Engineering
Area of study
Health
Education type
On campus
Timing
Full time
Course Language
English
Intakes
Program start dateApplication deadline
2026-09-01-
About Program

Program Overview


Artificial Intelligence and Digital Health MSc

Overview

The Artificial Intelligence and Digital Health MSc course is designed to equip students with the specialist knowledge and skills to succeed in the field of digital health and leverage AI to improve patient care. The course is interdisciplinary, focusing on the intersection of healthcare, AI, and big data, making it suitable for students from either a bioscience or computing background.


Course Structure

The course is divided into core and option modules, with a focus on experiential, interdisciplinary, and community learning alongside collaborative co-creation of solutions to evaluate innovative real-world policies and implement health and wellbeing practices.


Core Modules

  • Biobanking for Data Science: This module delves deep into the intersection of biobanking, personalised/precision medicine, and digital health. Students will gain an understanding of the principles and practices of biobanking and how digital tools and technologies are transforming its landscape.
  • Health Data Science: This module introduces students to the basic concepts of data science and its application in healthcare. The main module objective is to aid students in understanding and implementing the computational methods that help in extracting insights from healthcare datasets and developing new diagnostic, prognostic, predictive, or monitoring applications to address global health challenges.
  • Postgraduate Research Methods: This module teaches the principles and practice of research with a focus on study design and methods of data collection. It will show how these designs and methods can be applied to evaluation studies as well as to research.
  • Data Mining and Machine Learning: This module provides an overview of modern techniques in Machine Learning and Data Mining that are particularly customised for Data Science applications. Students will be introduced to a range of toolkits, such as R and Python, and explore the features and strengths of different machine learning and data mining methodologies using selected data sets related to specific public sector or businesses application domains.
  • Postgraduate Project: This module allows students to hone their research skills by undertaking a substantial piece of research. Within this module, students will learn how to formulate a research proposal, investigate an appropriate research topic, keep research records, analyse the results of their research, discuss the findings with reference to previously published work, and convey the importance of their research through effective communication.

Option Modules

  • Big Data Theory and Practice: This module offers a comprehensive exploration of Big Data's challenges, encompassing its inherent volume, velocity, variety, and veracity. It sheds light on both traditional SQL and evolving NoSQL database architectures, emphasising their pivotal roles in the contemporary data landscape.
  • Communicating Science: This module will explore topics in science communication and will help students develop transferable skills and perspectives in the many ways that scientific knowledge can be disseminated.
  • Data Visualising and Dashboarding: This module covers the theoretical and practical aspects of data visualisation, including graphical perception, dynamic dashboard visualisations, and static data ‘infographics’. Tools used include R and Tableau.
  • Science, Technology and Commercialisation: An in-depth study on the scope of commercial biotechnology and starting and financing an operational company.
  • Systems Biology: This module will introduce the theoretical and practical underpinnings of systems biology. The emphasis is on studies of entire systems assisted by the use of bioinformatics and how the knowledge from these may be applied to medicine.

Professional Recognition

The University of Westminster is a member of the Turing University Network, providing universities with an interest in data science and AI with the opportunity to engage and collaborate with The Alan Turing Institute and its broader networks.


Entry Requirements

  • A minimum of a lower second class honours degree (2:2) in a relevant discipline including health or allied health related subject, or in social care related subjects.
  • If the first language is not English, students should have an IELTS 6.5 with at least 6.5 in writing and no element below 6.0.
  • Applicants are required to submit one academic reference.

Fees and Funding

  • UK tuition fee: Tuition fees have yet to be confirmed for this course (Price per academic year)
  • International tuition fee: Tuition fees have yet to be confirmed for this course (Price per academic year)
  • There are a range of funding available that may help students fund their studies, including Student Finance England (SFE) and scholarships.

Teaching and Assessment

  • Teaching methods across all postgraduate courses focus on active student learning through lectures, seminars, workshops, problem-based and blended learning, and where appropriate practical application.
  • Assessments include written exams, practical, and coursework.

Research Groups

  • Health Data Science
  • Computational Vision and Imaging Technology

Supporting You

  • The Student Hub is where students will find out about the services and support the university offers, helping them get the best out of their time with them.
  • Study support, personal tutors, student advice team, and extra-curricular activities are available.

Course Location

  • The course is located at the Cavendish Campus, which offers state-of-the-art science and psychology labs and refurbished computer suites.
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University of Westminster


Overview:

University of Westminster is a public university located in London, England. It offers a wide range of undergraduate and postgraduate programs across various disciplines. The university is known for its focus on practical learning and its strong connections to the industry.


Services Offered:


Student Life and Campus Experience:

The university has four campuses across London, providing students with a vibrant and diverse campus experience. Students have access to various facilities, including a cinema, gallery spaces, and sports facilities. The university also offers a range of student support services, including career guidance, academic support, and mental health services.


Key Reasons to Study There:

    Location:

    The university's location in London provides students with access to a wealth of cultural and professional opportunities.

    Practical Learning:

    The university emphasizes practical learning, with many programs incorporating work placements and industry projects.

    Industry Connections:

    The university has strong connections to industry, providing students with opportunities for networking and career development.

    Diverse Student Body:

    The university has a diverse student body, creating a welcoming and inclusive environment.

Academic Programs:

The university offers a wide range of academic programs, including:

    Undergraduate courses:

    A broad range of undergraduate courses in various disciplines, including business, design, creative industries, and liberal arts.

    Postgraduate courses:

    A variety of postgraduate study options, including master's degrees, research degrees, and short courses.

Other:

The university has a strong commitment to research and innovation, with a focus on areas such as sustainability, social justice, and digital technologies. It also has a dedicated alumni network, providing support and opportunities for graduates.

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