Teaching
The MSt Healthcare Data Science is a part-time Master's course designed to fit with the demands of full-time employment. The course is delivered through a combination of face-to-face sessions requiring attendance in Cambridge (blended with remote learning where suitable), plus self-directed learning supported through a virtual learning environment [VLE].
The first module of the course is also available as a MicroMasters course on the edX platform, learners who finish the MicroMasters successfully are able to join the course from module 2 subject to providing a verified certificate. The learning outcomes and assessment for module 1 are covered in the MicroMasters.
The course is structured across the following modules:
Module 1 – Data driven decision making (15 Credits)
Module 2 – Principles of Health Data Science (15 credits)
Module 3 – Health Data Science II (15 credits)
Module 4 – Data Visualisation (15 credits)
Module 5 – Machine learning (15 credits)
Module 6 – Databases (15 credits)
Module 7 – Data analysis and inference (15 credits)
Module 8 – Advanced statistical methods (15 credits)
Module 9 – Research Dissertation (60 credits)
Each 15 credits of study is approximately equivalent to 150 hours of study which will consist of face-to-face teaching, blended, and self-directed learning. This is an indicative amount and it is recognised that individuals may engage in greater or lesser amounts of study for each unit.
| One to one supervision |
Students can expect around 12 hours of supervision, with contact at least termly, with increased frequency during the research project, this includes feedback and the reading of drafts. Each student will have a primary dissertation supervisor who will provide guidance on their research project for Module 9 in the second year of study. |
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| Seminars & classes |
The programme is delivered through a combination of in-person teaching sessions delivered on a week residential block with asynchronous teaching delivered via the course virtual learning environment. Examples of the type of teaching methods used include, but are not limited to, live and pre-recorded lectures, seminars, group discussions, online readings, quizzes, data handling exercises, group activities and discussion forums. Peer-to-peer learning forms are an important element of course teaching. |
| Lectures |
Most of the teaching on the Master's programme is interactive. Each 15 credits of study is equivalent to approximately 150 hours of study which will consist of face-to-face teaching, blended, and self-directed learning. Students are required to attend a full week of teaching for each module which approximately includes 37-40 hours of teaching, lectures and a range of other teaching and learning activities such as small group discussions, presentations and workshops. |
| Taught/Research Balance |
Predominantly Taught
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Feedback
Students will receive formative (does not contribute to final mark) and peer-based feedback throughout the course, along with tutor provided feedback on the submitted summative (will contribute to final mark) assignments.
Assessment
Thesis / Dissertation
A research project of 10-12,000 words will be required.
Essays
Students who have joined the MSt in Healthcare Data Science having successfully completed the MicroMasters via the edX platform, will have already met the assessment and learning outcomes for module 1 and therefore will only need to submit the summative assignments for modules 2-8 along with the research project.
Other
Assessment will be through a range of formats which may include coursework, written and oral examinations, posters, presentations and projects.