Computer Science courses

About Computer Science (artificial Intelligence) - Msc in University of Kent

This conversion course has been designed to provide graduates from any discipline with an introduction to computer science and artificial intelligence to develop skills to restart your career, get into technology and succeed within an evolving industry.

The Computer Science (Artificial Intelligence) course prepares graduates for a career in computing, or a career involving the application of computing and artificial intelligence for problem solving in any professional field. Working with the latest technologies, you develop skills which will boost your career prospects as computer science is used for problem solving in every industry. You can explore a wide range of personal interests and career ambitions through a wide range of optional modules.

Graduates from our computer science conversion courses have successfully achieved careers with leading software, technology, and commercial global companies such as IBM, Cisco, Logica/CMG, Pfizer, Reuters, Shell and Zurich Financial.

This programme is available with an optional industrial placement of between eight and 50 weeks. The industrial placement provides an opportunity to work in real-world, technical and business roles, enhancing your study experience and having a dramatic impact on your choices after graduation.

Academic qualification equivalents

• For entry to a Kent postgraduate degree programme (Master’s), Indian students typically need to have completed a three or four-year undergraduate degree (Honours Bachelor degree or Professional degree) at an accredited university or college. General Bachelor degrees may sometimes be considered. Exact requirements will depend on the postgraduate degree you are applying for and the institution you have studied at.

English language requirements 

• IELTS : 6.5 overall (with a minimum of 6.0 in R&W; 5.5 in S&L)
• TOEFL IBT: 90 overall (with a minimum of 22 in R; 21 in W; 17 in L; 20 in S
• PTE: 62 overall with 60 in each subtest

Highlights

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