Structure
The below structure outlines the proposed modules for this programme. Programme and module specifications provide full details about the aims and objectives of each module, what you will study and how the module is assessed.
- Structure of the year
Term 1 (September - December) consists of ten teaching weeks for AB1 slot modules, plus one Reading Week* in the middle of the term. Followed by the Winter break.
Term 2 (January - March) consists of a further ten weeks of teaching for C and D slot modules, plus a Reading Week in the middle of the term. C modules are taught in five half-week blocks before Reading Week. D modules are taught in five half-week blocks after Reading Week. Followed by the Spring break.
Term 3 (April - September) consists of the project report.
*Reading Week is a week during term where no formal teaching takes place. It is a time for private study, preparing for assessments or attending study/computer skills workshops. There are two Reading Weeks at LSHTM: one in November and the other in February.
- Term 1
All students take five compulsory AB1 modules:
- Concepts and Methods in Epidemiology
- Health Data Management
- Programming
- Statistics for Health Data Science
- Thinking Like a Health Data Scientist
- Term 2
Students take a total of four study modules, one from each timetable slot (C1, C2, D1, D2).
C1 slot
- Machine Learning (compulsory)
C2 slot
- Data Challenge (compulsory)
D1 slot
- Analysis of Hierarchical and Other Dependent Data
- Genomics Health Data
- Modelling & the Dynamics of Infectious Diseases
D2 slot
- Analysis of Electronic Health Record Data
- Bayesian Analysis
- Term 3: Project report
Students will start working on their summer project mid-April for submission by early September. The project will typically involve identifying appropriate data to tackle a particular research question, extracting and cleaning the data, analysing the data and creating suitable visualisations of the results. Students will describe the whole project in a detailed written report.
Please note: Should it be the case that you are unable to travel overseas or access laboratories in order to complete your project, you will be able to complete an alternative desk-based project allowing you to obtain your qualification within the original time frame. Alternatively, you will be able to defer your project to the following year.
Teaching methods
As well as traditional lectures followed by problem-based practical sessions, with or without computers, teaching will include:
- Flipped classroom approaches where students are provided with materials to read/watch independently, followed by formative assessment in class to assess understanding (e.g. via Moodle-based multiple choice questions), allowing contact time to focus on practical problem-based learning.
- Interactive lectorials, alternating lecture-based and hands-on practical sessions.
- Panel discussions and workshops, to stimulate debate particularly for current live controversies such as the ethics of algorithms.
- Teamwork, particularly in the team-based module and the datathon.
- Opportunities to develop and practice professional skills, including a range of student-led presentations, modules which require student teams to interact with a client (someone who is not a data scientist working outside of the LSHTM who wishes to “employ” our students to address a particular research question).
Changes to the programme
LSHTM will seek to deliver this programme in accordance with the description set out on this programme page. However, there may be situations in which it is desirable or necessary for LSHTM to make changes in course provision, either before or after registration. For further information, please see our page on changes to courses.