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Key information
DATA SOURCE : IDP Connect

Qualification type

MSc - Master of Science

Subject areas

Informatics

Course type

Taught

Course Summary

Overview:

This newly developed Data Science course will provide you with the technical and practical skills to analyse big data that is key to success in future business, digital media and science. Study industry-specific topics and specialise in areas such as data mining, machine learning, data analytics and visualisation, and security of big data.

Our close links to industry and businesses in the North East, as well as the research expertise of our academics, makes this course unique and ensures that the course structure is developed according to the needs of the employment sector.

Course structure:

We use a wide variety of teaching and learning methods which include lectures, group work, research, discussion groups, seminars, tutorials and practical laboratory sessions. Compared to an undergraduate course, you will find that this Masters requires a higher level of independent working.

Assessment methods include written reports and research papers, practical assignments and the Masters project.

Different course options

Full time | University of Sunderland | 12 months | 14-SEP-20

Study mode

Full time

Duration

12 months

Start date

14-SEP-20

Modules

Learn how to use different types of data and understand how to fuse more than one dataset together. Apply a full range of traditional and intelligent analytics to a variety of datasets and make use of modern data science / big data platforms and languages. Cover techniques and tools for presenting and visualisation.

Tuition fees

UK fees
Course fees for UK / EU students

For this course (per year)

£7,250

Average for all Postgrad courses (per year)

£5,202

International fees
Course fees for non-UK / EU students

For this course (per year)

£14,000

Average for all Postgrad courses (per year)

£12,227

Entry requirements

Students need to hold an undergraduate degree with a classification of 2:2 or above in a computing or related discipline (mathematics, statistics, engineering), or 2:1 or above in a relevant non-computing or related discipline (which has numeracy included and/or application of big data as a significant theme).