Data Science University of Hertfordshire

Overview

The Data Science programme from University of Hertfordshire  is distinctive in its philosophy of widening participation and provides a route to gain skills and training in data science to those from a background not traditionally associated with the STEM-themes of mathematics, statistics and programming. The programme is designed to be appealing to a broad range of students who are seeking training or up-skilling in data science.

Key facts

• You will benefit from the expertise of astrophysicists, physicists, mathematicians and computer scientists with international research profiles. Their day-to-day research involves application of, and in some cases the development of new, data science skills, from fundamental statistical analyses, the use of distributed high-performance computing, and research into novel artificial intelligence algorithms.

• We aim to make the programme distinctive in terms of the mixture of hard and soft skills, and the close personal relationship that we are developing with employers, which will feed into the programme through continuous assessment of the latest industry-relevant tools, which are continually evolving as new technology and software becomes available.

• You will experience a multidisciplinary approach to data science by experiencing challenges in computer science, creative arts, medical and business environments.

• You will have the opportunity to attend a wide range of research-focused seminars to excite and spark your intellectual curiosity.

Careers

Upon completion of the programme you will be able to demonstrate (and apply) an understanding of a wide range of theoretical and practical skills enabling you to enter a variety of disciplines and industries. You will be able to:

• Understand and be able to critically assess the various strengths and weaknesses inherent to different data science methodologies.

• Design creative strategies and solutions to tackle unfamiliar data science problems and critically assess outputs and results through appropriate statistically robust validation and other performance assessment techniques.

• Effectively communicate problems, methods, results and conclusions through oral and written presentation to both expert and non-expert audiences.

• Have an appreciation of both the underlying research behind data science techniques (e.g. cutting-edge algorithms and computational techniques) and their relevance and application across a broad range of disciplines.