Scholarships

Data Science and AI MSc conversion programme Scholarships 2020-21

About the award

Level: Masters
Course: MSc in Data Science and AI conversion programme (Full Time)
Country: All
Value: £10,000
No. of awards: 3
Deadline: 12 August 2020 for September entry; 12 November for January entry.

More information

Queen Mary University of London has secured funding from the UK Office for Students to offer three scholarships of £10,000 each to students studying the MSc Data Science and Artificial Intelligence conversion programme. These scholarships are for under-represented groups in the field of Data Science and Artificial Intelligence.

To apply for the scholarship, you must have already submitted your application for the MSc Data Science and Artificial Intelligence programme and have a Queen Mary University of London Student ID, which is assigned to you as part of the MSc application process.

Eligibility criteria

To be eligible you need to be a member of at least one of the under-represented groups mentioned below. All applications will be assessed and priority will be given to those in groups a, b and c. Preference will also be given to students resident in the UK, but not exclusively so. In the event that we have more eligible applicants than scholarships, funding decisions will be made based on your written personal statement provided in your scholarship application as well as on academic merit.

a. Female student

b. Black student

c. Registered disabled students

d. Students from POLAR Q1 and Q2 (UK students only) [1]

e Care experienced student (UK students only) [2]

f. Estranged student (UK students only) [3]

g. Gypsy, Roma, Traveller student (resident in the UK)

h. Refugees (resident in the UK)

i. Children from UK military families, veterans and partners of UK military personnel.

[1] POLAR is an acronym for Participation Of Local Areas, which is used in the UK Higher Education (HE) sector as a measure of disadvantage? It is founded on postcode. The UK Government compiles statistics on how many young people in different postcode regions typically go into HE. The 20% of areas with the lowest participation rates are designated as “Quintile 1” (Q1), the top 20% are “Quintile 5” (Q5), and so on. To find out whether your home address is within a Q1 or Q2 area check out: https://www.officeforstudents.org.uk/data-and-analysis/young-participation-by-area/.

[2] A "Care Experience Student" is a student who has been, or is currently, in care, or from a looked-after background, at any stage in their life, no matter how short, including adopted children who were previously looked-after. This care may have been provided in a one of many different settings such as in residential care, foster care, kinship care, or through being looked-after at home with a supervision requirement.

[3] The UK Office for Students defines "estranged students" as students between the age of 18 to 24 who are not communicating with either of their parents. Consequently these students lack the support of a wider family. They may be estranged before entering higher education but can also be at risk of estrangement or becoming estranged during their studies.

How do I apply?

You must have already applied for the MSc Data Science and Artificial Intelligence conversion programme. You will need a Queen Mary University of London student ID number (which you receive as part of the application process) to be able to apply for the scholarship.

Please download the application form here OfS Scholarships Application Form [DOC 192KB] and read the information for applicants document before completing your application. DataScienceandAI_InformationforApplicants [PDF 693KB]

You should submit your completed application form to: ioc@qmul.ac.uk

Deadlines: 12 August 2020 for students starting the programme in September 2020.

For students applying for the January 2021 intake, you should submit your application before 12 November 2020.

Contact

Queries relating to this scholarship can be addressed to the Institute of Coding at Queen Mary University of London at ioc@qmul.ac.uk.