Full-time: One year; Part-time: Two years
Campus, Full-time and Part-time
The ongoing progress in mathematical tools and automated data acquisition, combined with the growth of very large datasets and computational power, has established data science as a mature discipline with a rapidly growing number of exploitations in a wide spectrum of applications.
This course has been carefully planned and is designed to develop your knowledge and skills in modern data science practice. It combines a project-based approach with opportunity for personal development through a flexible and supportive learning experience.
Four key strands:
The underpinning practical and theoretical skills, such as programming, project management, statistics, develop along with timetabled teaching sessions and individual/group projects.
2.2 or above in science, technology, engineering and math subjects, or equivalent industrial experience (interviews might be required)
Find out more about Postgraduate courses at our Postgraduate Advice Event on 23 June 2019
You will benefit from a dedicated data science lab for teaching purposes in the new Engineering Innovation Centre (EIC) building. Equipment includes modern graphics processing unit (GPU) cards for running simulations of data science projects.
The course team has domain experts with expertise in data science and related applications such as medical imaging, signal analysis for non-invasive inspection, computer aided diagnosis.
You can apply for many of the postgraduate UCLan courses using our Online Application System.
Full-time: £6,700 per year (UK/EU)
Part-time: £3,345 per year for first 2 years (UK/EU)
Tuition Fees are per year unless otherwise stated.
For 2018/19 fees please refer to our fees page.
The course team is working closely with the ADMT (Advanced Digital Manufacturing Technology) research centre and the CVML (Computer Vision and Machine Learning) research group.
As a student you can benefit from working with the modern manufacturing facilities including for example Kuka robots and working on real-life manufacturing projects through the industrial links. Furthermore, through the collaborative links with the health sector, interested students could contribute to the ongoing biomedical data analytics projects.