- General welcome
Greetings and a warm welcome to LSHTM and the MSc Health Data Science programme! From "Hello" to "Nǐ hǎo," we are thrilled to have you here.
Over the coming months you will learn the vital knowledge and skills that are required to have a role in this exciting and fast-changing new discipline. You will be taught how to manage, analyse and interpret electronic health data in a way that can make a valuable difference to health outcomes. As well as developing practical skills in statistics, programming and machine learning you will also learn about the ethical and security considerations of personal information, the epidemiological foundations of robust health research and how to effectively communicate your findings to different audiences. Our experienced academic team are here to inform, guide and support you through every step of your studies.
If you have any questions, concerns or problems then please don’t hesitate to get in touch with us at hds_DamienKeith@lshtm.ac.uk and we will do all we can to help.
Damien Tully and Keith Tomlin, HDS Programme Directors
- Welcome Week
- Personal tutors
At the start of the programme you will be allocated a personal tutor with whom you will meet regularly throughout the year. We would be very grateful if you could take five minutes to complete this brief questionnaire. It will help us to learn a little more about you before teaching begins, and to assign you a personal tutor who may match your research interests.
When you arrive at LSHTM, your Programme Director will invite you to participate in a Pentacell, an activity that strengthens the School community, increases our wellbeing and forwards our goal of improving global health. The idea is deceptively simple – five students meet weekly for five weeks and listen to each other’s ideas and perspectives. It’s not compulsory but is strongly recommended by our School's Director, Liam Smeeth, Programme Directors, Student Support Services and former students.
Feedback from previous students:
“It was nice to be able to connect with people on my programme that I wouldn't necessarily have spoken to otherwise and it was a good way to make friends at the beginning.”
“Our Pentacell group chose not always to follow the exact guidelines for each session, and instead focused more on getting to know members of our programme better. I enjoyed the Pentacell experience we had, as it helped develop interpersonal relationships with those on my programme.”
“It gave me an opportunity to meet people on my programme. It also helped me develop interpersonal skills.”
“Very pleasant experience especially for me as an international student who moved to the UK for the 1st time.”
- Preparing for the programme
You will be pleased to know we do not have a set reading list for this programme. As pre-course reading we recommend that you read and try examples in this Maths, Probablity and Statistics Refresher.
You will require a personal laptop computer running an up-to-date version of Microsoft Windows or Apple macOS. For Windows laptops the minimum version is Windows 10. For Mac laptops the minimum version is MacOS Big Sur. We would suggest a minimum of 16GB of memory and 512GB of storage space. If your laptop has a lower specification then you may need to log into one of the School’s more powerful remote servers to study some modules. If you have any concerns about the requirements please do contact us. All of the required software (including MS Office 365) will be made available to you without charge.
- Information for new part-time students
In the first year, part-time students are expected to attend the programme for 2.5 days per week. During this time you will complete the following modules: Introduction to Health Data Science, Statistics for Health Data Science and Programming. In year 1 you are expected to be present for classes on Monday and Tuesday and the following sessions: Thursday 26th October (Programming), Wednesday 1st November (Thinking like a Health Data Scientist) and Thursday 2nd November (Programming).
In the second year, students will take classes on a Thursday and Friday. During this time you will complete Health Data Management and Concepts and Methods in Epidemiology.
In Term 2 of the first year you will take the compulsory module in Machine Learning and an optional module chosen from Genomics for Health Data Science (recommended), Spatial Epidemiology in Public Health, Modelling and the Dynamics of Infectious Diseases or Analysis of Hierarchical and Other Dependent Data.
In the second year you will take the compulsory Data Challenge module and an optional module chosen from Analysis of Electronic Health Records (recommended), Environmental Epidemiology or Bayesian Analysis.
- Health Data Science modules
In the table below you can see the compulsory and optional modules for the MSc Health Data Science. There are no optional modules in Term 1 and you will be required to select two optional modules in Term 2. We highly recommend you select the Genomics Health Data and Analysis of Electronic Health Records optional modules in Term 2 as we believe these will give essential skills needed in the Health Data Science field.
Term Compulsory modules Optional modules Term 1 (weeks 1-10)
- Thinking like a Health Data Scientist
- Health Data Management
- Concepts and Methods in Epidemiology
- Statistics for Health Data Science
N/A Christmas break Term 2 (weeks 11-20)
- Machine Learning
- Data Challenge
Select one from:
- Genomics Health Data (Recommended)
- Modelling and the Dynamics of Infectious Diseases
- Analysis of Hierarchical and Other Dependent Data
- Spatial Epidemiology in Public Health
Select one from:
- Environmental Epidemiology
- Bayesian Analysis
- Analysis of Electronic Health Records (Recommended)
Easter break Term 3
- Summer project
- Health Data Science module schedule
In Term 1 there are four module slots called A1, A2, B1 and B2:
Slot When Period Length A1 Before Reading Week Mon (AM) to Wed (lunchtime) 5 weeks A2 Before Reading Week Wed (PM) to Fri (PM) 5 weeks B1 After Reading Week Mon (AM) to Wed (lunchtime) 5 weeks B2 After Reading Week Wed (PM) to Fri (PM) 5 weeks
Term 1 timetable
Date Week Mon AM Mon PM Tue AM Tue PM Wed AM Wed PM Thu AM Thu PM Fri AM Fri PM 25-Sep Epi 2-Oct 1 Thinking Stats Prog Prog HDM HDM Epi Epi 9-Oct 2 Thinking Thinking Prog Prog HDM HDM Epi Epi 16-Oct 3 Thinking Stats Prog Prog HDM HDM Epi Epi 23-Oct 4 Thinking Thinking Prog Prog Prog Prog Epi Epi 30-Oct 5 Thinking Stats Prog Prog Thinking Thinking Prog Prog Reading week 13-Nov 6 Thinking Stats Stats Stats HDM HDM Epi Epi 20-Nov 7 Thinking Thinking Stats Stats HDM HDM Epi Epi 27-Nov 8 Stats Stats Stats Stats HDM HDM Epi Epi 04-Dec 9 Stats Stats Stats HDM HDM Epi Epi 11-Dec 10 Stats Stats Stats HDM HDM
Thinking - Thinking like a Health Data Scientist
Stats - Statistics for Health Data Science
Prog - Programming
HDM - Health Data Management
Epi - Concepts and Methods for Epidemiology
Blank - free self-directed study time
In Term 2 there are four module slots called C1, C2, D1 and D2:
Slot When Period Length C1 Before Reading Week Mon (AM) to Wed (lunchtime) 5 weeks C2 Before Reading Week Wed (PM) to Fri (PM) 5 weeks D1 After Reading Week Mon (AM) to Wed (lunchtime) 5 weeks D2 After Reading Week Wed (PM) to Fri (PM) 5 weeks
Term 2 timetable
Date Week Mon AM Mon PM Tue AM Tue PM Wed AM Wed PM Thu AM Thu PM Fri AM Fri PM 8-Jan 1 ML ML ML ML Chall Chall Chall Chall Chall 15-Jan 2 ML ML ML ML Chall Chall Chall Chall Chall 22-Jan 3 ML ML ML ML Chall Chall Chall Chall Chall 29-Jan 4 ML ML ML ML Chall Chall Chall Chall Chall 05-Feb 5 ML ML ML ML ML Chall Chall Chall Chall Chall Reading week 19-Feb 6 Geno Geno Geno Geno AEHR AEHR AEHR AEHR AEHR 26-Feb 7 Geno Geno Geno Geno AEHR AEHR AEHR AEHR 04-Mar 8 Geno Geno Geno Geno AEHR AEHR AEHR AEHR 11-Mar 9 Geno Geno Geno Geno AEHR AEHR AEHR
18-Mar 10 Geno Geno Geno Geno AEHR AEHR AEHR AEHR
ML - Machine Learning
Chall - Data Challenge
Geno - Genomics Health Data (or other option chosen)
AEHR - Analysis of Electronic Health Records (or other option chosen)
Blank - free self-directed study time
- Summer research project
A major component of your MSc will be a summer research project that, for full-time students, will be carried out between April and August. Health Data Science projects usually involve the management, analysis and interpretation of real data that may come from electronic health records, clinical trials or epidemiological studies. These data may come from an external project partner (see below). Projects focusing on improving data analytics via the design of apps or other software may also be permitted.
A list of potential projects will be circulated in late November. You should discuss these with your personal tutor and select your top 3 preferences. Projects will then be allocated by the end of December. While it is not always possible to assign each student their first choice we try to make sure that everyone is happy with their allocated projects.
Students may also arrange projects directly with the tutor or may be provided with a project topic and data from an employer. Please note that we can’t guarantee that these datasets will be suitable for your project so please discuss with the Programme Directors if you have your own data that you would like to explore.
Part-time students can split the project over two years or complete it in the second year. Please talk to the Programme Directors if you wish to complete the project in its entirety in Year 2 as it requires 600 hours of learning time. In either case you will submit your project at the end of Year 2.
External project partners from previous years have been drawn from across the health data science landscape and include the following as examples: The Health Foundation, GSK, Pfizer, Deloitte, IQVIA, Medicines and Healthcare products Regulatory Agency, National Institute for Health Protection, Office for National Survey (ONS), the United Nations and the UK Health Security Agency.
Page last updated September 2023