Provider case study: Postgraduate conversion courses in artificial intelligence and data science

University of Greenwich

The MSc degree in Data Science and its Applications at the University of Greenwich is designed to provide students with a solid grounding in data science theory and practice.  

The course is tailored to transform professionals from a wide range of backgrounds, mostly non-STEM (science, technology, engineering and mathematics) into accomplished data scientists. These professionals would then be well-placed to enhance their existing careers with an expansive set of data science skills, to move fully into data science roles, or to pursue further data science specialisations.

A goal of the course is also to increase the diversity of qualified data science experts. Thanks to dedicated funding from the Office for Students, in the first two years of running the programme we were able to offer scholarships to support students from groups that are underrepresented within data science, with priority for women, black students, disabled students and students from lower socioeconomic backgrounds. 

Course design

The course provides students with the knowledge of foundational data science concepts, including programming, statistics, machine learning and data ethics and governance. This is followed by the opportunity to further specialise in specific topics of their choice, which were selected to leverage the expertise of our staff and the current needs of data science professionals.  

As part of their learning, students apply their data handling skills to real-world problems in a variety of applied fields. This approach allows them to practise and showcase their ability to critically analyse, solve and evaluate data-heavy projects – skills that are vital for both employment and further studies. Through an emphasis on applications, social impact, and peer supported learning, the course aims to highlight how entering data science from a non-STEM background can be a strength, because data science is as much about domain expertise – and, crucially, people – as it is about numbers.  

The journey has not been without its challenges, and it would have been impossible without the dedication and expertise of all the educators and professionals that helped setup, deliver and improve the course. The experience of teaching on the course has been very rewarding, including because all the students have been bright, motivated and open with sharing their feedback with us. Engaging in a consistent and mutual dialogue between students and staff has been crucial to the course’s achievements and one of the best parts of the whole endeavour.  

Student experience and impact

Maxine is a current data science student and scholarship recipient. 

‘Data science is a high-end career normally for the intellectually gifted and for students with a science academic and mathematical background. A career in data science is aimed for high academic achievers. 

‘Hence it would have been incredibly difficult for a person like me to study data science with a media communications degree, even though I have achieved awards within filmmaking and had some success within my career in social housing and local government. 

‘I would like to say a big “thank you” to you all, on behalf of every student on this course, especially for those students who like me received a scholarship, for making it possible for us to study a postgraduate degree. 

‘The reason why I want to become a data scientist is because there is a great need within British local governments for academically trained data scientists to steer the direction of data within departments. The methods local governments use to collate, interpret, and utilise data can be improved.  

‘I also desire to encourage people who are from marginalised backgrounds or have disabilities like me that they can make their lives better. I also would like to integrate data science within poorer communities and encourage young women from disadvantaged backgrounds to take up the profession because they are clever and bright.'

The future

Looking forward, students' feedback will keep being instrumental in improving the course. Priority will be given to improve students' experiences at the start of the course. Having a wide variety of prior backgrounds means that everyone's journey, especially at the beginning, can look different. This can be a strength, but only if appropriately leveraged. So, the focus will be on creating targeted on-boarding resources that can facilitate students' acclimatisation to the course, expanding from similar learning materials released earlier this year. 

More broadly, the course will continue to focus on teaching both foundational and latest data science skills. It will need to evolve, taking into account societal needs, research developments, and industry trends, to keep striking the right balance, still prioritising the teaching of data science principles and applications that benefit people – all people – and the planet.