Data Science MSc

University of Leicester

The programme will suit you if you are keen to solve interesting real-world problems using a data-focussed approach, have effective communication skills and an interest in computer programming. You don’t need to have high level maths or computing skills to enrol on the programme, although if you have a degree in either mathematical sciences or computing you will be able to develop your specialist skills further.The programme requires a willingness to develop your understanding of mathematics and we will provide access to resources and a range of support to develop your skills. You will have access to materials tailored to the maths need of each module, either within the module teaching itself, for from other University resources such as our Centre for Academic Achievement.Similarly, we have resources to give you familiarity with Python and R, the two high-level computer languages used on the course. We will provide you with a suitable laptop to help you on your data journey.The programme will give you the skills to use cutting-edge data science tools to solve important problems in a wide range of areas (science, business, the arts and culture, government). The programme focus is just as much on the skills of creative and ethical problem solving as it is on developing technical skills.We recognise that all of us will need to work with artificial intelligence tools in the future, and for the benefit of humanity data science teams must include experts with a diverse range of skills, experience and background. Also, recent developments in programming tools coupled with intelligent use of AI assistance mean that it is not necessary to have a degree in computer science or mathematics to develop powerful data-driven solutions in a professional and ethical manner.Many data science models are already built wholly or partly by artificial intelligence. We’ll teach you to use the output of artificial intelligence tools (such as large language models like ChatGPT and modern program development environments) in an effective and professional way, enabling you to quickly design and implement data science workflows appropriate for a wide range of tasks. We place as much emphasis on choosing models that are appropriate for a particular circumstance and validating model outputs and auditing consequences of model use as on the coding for the model.