Data Science and Analytics
We have designed this course in consultation with industry partners. This has enabled us to understand their needs for Data Scientists, what skills will be required and on successful completion of this course individuals will be highly employable within businesses.
The course will be of specific interest to: a mathematics graduate wishing to use your skills in a vocational business based environment; a computer science graduate wishing to follow a vocational route; individuals currently working in Business and looking to grow their career through gaining Data Science and Business Analytics skills.
You will be assigned a Personal Tutor from the start of your course who will work with you throughout your studies to help you achieve your academic best. The knowledge we provide you with in these areas will give you all of the essential know-how on methods, tools and techniques to deliver in your career as a Data Scientist. We believe Data Science is very much an intellectual 'contact sport' and through this course we provide you with every opportunity to put your theoretical knowledge into practice.
The project work we have imbedded within the course has been chosen and developed based on real-world scenarios across a range of industry and government sectors and is specifically designed to: provide an essential link between your theoretical learning and real-world challenges; create an environment where you decide the methods and tools best suited to the challenge based on what you have learnt; recreate some of the challenges facing industry and Government today and those very similar to what you will encounter in the workplace as a Data Scientist; be adaptable to reflect new methods / tools and scenarios in this fast developing discipline; be able upon completion of the projects to reference your experience in working with such challenges.
A 2:2 in an appropriate course with a B grade in Maths at A level or equivalent.
Data Science Foundation (20 credits)
Managing Data (20 credits)
Data Exploration and Analysis (20 credits)
Mathematics (20 credits)
Machine Learning and Cognitive Computing (20 credits)
Data Visualisation and Presentation (20 credits)
Dissertation/Project (60 credits).
Assessment includes a dissertation.
|Qualification||Study mode||Start month||Fee||Fee locale||Course duration|
|MSc||Full-time||September 2017||8,160 (Year 1)||Home/EU||1 1 Years|
|MSc||Part-time||September 2017||2 2 Years|
|MSc||Full-time||September 2017||12,600 (Year 1)||International||1 1 Years|
|MSc||Full-time||September 2018||12,600 (Year 1)||International||1 1 Years|
|MSc||Full-time||September 2018||8,160 (Year 1)||Home/EU||1 1 Years|
|MSc||Part-time||September 2018||2 2 Years|
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