Applied Data Science (Renewable Energy) The University of Exeter

Overview

Key Selling Points

• Our Centre for Computational Social Science uses the latest quantitative methods to study real-world issues in social sciences

• Partner to the Alan Turing Institute

• Home to Exeter's Institute for Data Science and Artificial Intelligence

• Courses developed in collaboration with industry, using current methods, platforms, software and real-world data

Throughout this Applied Data Science (Renewable Energy) MSc programme from The University of Exeter you'll:

• Become immersed in the ‘Big Data revolution’ and develop state-of-the-art data science and AI skills alongside expertise in emerging renewable technologies

• Designed in collaboration with internationally renowned researchers and industrial partners you will benefit from research-led teaching, practical examples and hands-on activities

• Develop sought-after, discipline-transcending skills and utilise these through use of modern scientific computing software and a new state-of-the-art Renewable Engineering Facility

• Gain the skills and experience you need to succeed in the demanding and fast growing data analytics sectors, specifically within renewable technologies, on the Applied Data Science (Renewable Energy) programme from The University of Exeter.

Career opportunities:

This programme is designed for students from a variety of backgrounds who want to enhance their understanding of and confidence in using real-world data and mathematical models to address the big environmental, social and economic issues of our time, gaining knowledge of local to global scales and tapping into/skilling-up for the “Big Data revolution” in the field and in general.

• Data science, modelling and essential programming skills and how to apply these to The United Nations sustainable development goals

• Ability to extract information from data as a basis for evidence-based decision making

• Working with stakeholders and advanced team-working skills in complex organisations

• Using digital media to convert complex data sets into useful information to engage with the lay public and specialists