|Study location||United Kingdom, Sheffield|
|Type||Master courses, full-time|
|Nominal duration||1 year|
|Tuition fee||£20,470.00 per year|
Undergraduate diploma (or higher)
- Minimum 2:1 honours degree in a numerate discipline (mathematics, economics, accounting, engineering, physics, chemistry)
The entry qualification documents are accepted in the following languages: English.
Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original.
IELTS: 6.5 with a minimum of 6.0 in each component
At least 2 reference(s) must be provided.
These should be supplied and signed by academic staff at institutions where you have studied previously. They must be presented on the official letter-headed paper of the relevant institution. If you have been out of education for the last two years, you can send one academic reference plus one from your current employer if you wish.
Learn how to manage vast amounts of information and transform it into actionable knowledge.
Our MSc Data Analytics will show you how to apply your previous academic experience to the real world and develop the skills needed to work with large quantities of data.
You’ll analyse the types of data sets that need to be interpreted in the modern world, including large data sets as well as structured and unstructured data.
The course draws on techniques from a range of disciplines, including computer science, artificial intelligence, mathematics and statistics.
It is designed for students with a numerate background (for example a first degree in mathematics, engineering, physics, chemistry or other physical sciences) as well as graduates already working in industry.
Scalable Machine Learning
Machine Learning & Adaptive Intelligence
Natural Language Processing
Industrial Team Project
Individual Data Analytics Dissertation
Information Governance and Ethics
Mathematical Data Science
Computer Security and Forensics
Parallel Computing with Graphical Processing Units (GPUs)