Computer Science and Artificial Intelligence, BSc (Hons)

Course summary

Reasons to choose Kingston

• To set the material in context as well as inspire our students, we invite leading practitioners from industry, such as Google and IBM, to give guest lectures and workshops.

• There's the opportunity for a year's work placement. This will give you valuable experience and help prepare you for a career in finance or data analysis.

• You'll use applications that model the real world and industry-standard software such as Python R, Matlab and SAS

About this courseThe course is ideal for students who are interested in developing and applying problem-solving skills to real world problems, would like to develop their understanding of computing, mathematics and statistical techniques through the practical lens of artificial intelligence (AI). With a balance of solid theory and practical application, this course builds on knowledge in relevant areas of statistics, data analysis, probability and programming.The over-arching aim of the Computer Science and Artificial Intelligence course is to produce highly trained graduates with specialist technical knowledge in the mathematical and computational science aspects of applied AI, capable of solving real world problems with understanding of the wider socio-technical implications. Future Skills Embedded within every course curriculum and throughout the whole Kingston experience, Future Skills will play a role in shaping you to become a future-proof graduate, providing you with the skills most valued by employers such as problem-solving, digital competency, and adaptability. As you progress through your degree, you'll learn to navigate, explore and apply these graduate skills, learning to demonstrate and articulate to employers how future skills give you the edge. At Kingston University, we're not just keeping up with change, we're creating it. Career opportunitiesThis degree is excellent preparation for a wide variety of careers, such as a data analyst, machine learning engineer, systems and business analyst, software engineer, programmer and network specialist.

Modules

Example modules– Principles of Data Analytics for AI – Applied AI and Machine Learning – Mobile Application DevelopmentTo view the full list of modules, please visit the University course webpage.

Assessment method

Assessment typically comprises exams (e.g. test or exam), practical (e.g. presentations, performance) and coursework (e.g. essays, reports, self-assessment, portfolios, dissertation).

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Example modules – Principles of Data Analytics for AI – Applied AI and Machine Learning – Mobile Application Development To view the full list of modules, please visit the University course webpage.