MSc Data Science
Course Aims
This innovative master’s in data science is an opportunity for graduates from a broad range of disciplines to develop data science skills. Our goal is to help you develop into an agile, skilled data scientist, adept at working in variety of settings and able to meet the challenges and reap the rewards of interdisciplinary team work.
Course Description
Background
The range of pathways reflects the interdisciplinary nature of the programme and we welcome applications from students with backgrounds in a wide range of disciplines including:
- business and management
health science- social sciences
- geography
- planning
- computer science
- mathematics
Skills and Training
We provide training in core data science skills, embedded in a disciplinary context provided by the pathway, and expect students to develop:
- computational skills
- data analytical skills
- data stewardship skills and knowledge
- project design skills
Elizabeth Burroughs, Chief Operating Officer at HACE
“HACE has worked with the University of Manchester on their MSc Applying Data Science programme for a number of years now and we have seen immense value from each of the projects. Students on the course always show passion for both data science and the mission that HACE is working towards; sustainably eradicating child labour in company supply chains. They continue to bring fresh, innovative and inspiring perspectives to our work and have been invaluable to the growth of HACE. University staff and academic advisors have been knowledgeable and generous with both their time and expertise. As a small start-up, the programme’s collective contribution to the business has been critical and we can’t wait to see where our ongoing partnership with the University and their students goes.”
Jake Dascombe, Actuary in the Government Actuary’s Department
The Project
Understanding the features of our clients’ pension data is crucial for the Government Actuary’s Department (GAD) to provide high quality advice and valuable insights. We worked with university students on this project as part of our ongoing collaboration with the University of Manchester.
The purpose of the project was to perform exploratory data analysis for one of our pension schemes and identify the principal factors that determine the fraction of pension a member decides to give up in return for a lump sum payment at retirement. Then to create at least one model using machine learning to predict the proportion of pension we expect members to commute at retirement.
Introduction and Engagement
Prior to the project we shared a project specification document with the students. It included background information, and a high-level overview of the steps expected to be carried out when processing, analysing the data and modelling.
We arranged an initial meeting to discuss the overall project. We had weekly meetings to discuss progress, any questions the students had, difficulties they encountered and expectations for progress made by the next meeting.
This ensured we had consistent dialogue which would help them to produce as high-quality report as possible in a relatively short time.
Students’ Performance
The students were exposed to a large dataset which can be complex to understand. Despite this and time constraints, they processed the data to the point where the datasets were fit to be used for data exploration.
The students successfully solved our real-world problem by performing a breadth of exploratory analysis. The insights of this and from their model will help us to better inform our clients.
The students also started to develop crucial analytical skills, which included always considering the bigger picture and the trade-off between breadth and depth of analysis.
These skills are vital to master to ensure the work produced is of maximum value to the client or reader. When reviewing the project with the students, we identified some key features which would have been advantageous to have been analysed in greater depth. This is something we will continue to encourage students to consider in future GAD projects.
Overall, it was a pleasure supervising the students and seeing the challenges they overcame and the quality of their presentation.
Project lead: Jake Dascombe is an Actuary in the Government Actuary’s Department where he works in the Analytical Solutions Team.