Data Science (with Specialisation in Artificial Intelligence) PgDip (Initial Qualification)

Our Data Science (with Specialisation in Artificial Intelligence) MSc provides a comprehensive grounding in the knowledge and skills required to work within Data Science and Artificial Intelligence.<br/><br/>**About this course**<br/>Artificial Intelligence is all around us and used on a daily basis. All Artificial Intelligence uses data analytics as the underpinning science. Throughout your degree you will explore the key topics in the field of Artificial Intelligence and develop the skills needed to address real problems in a given application area.<br/><br/>This MSc forms part of the following suite of data science courses:<br/>Data Science MSc, PGDip, PGCert<br/>Data Science (with Specialisation in Statistics) MSc, PGDip, PGCert<br/>Data Science (with specialisation in Visualization), MSc, PGDip, PGCert<br/><br/>**What youll learn**<br/>Through this course youll receive a comprehensive grounding in theory and application of data science. Youll also gain the ability to apply these skills to real problems in a given application area. Through project work, youll experience the full lifecycle from design of interactive visualization to experimental evaluation of an advanced visualization approach.<br/><br/>Topics covered in the course include:<br/>data visualization<br/>cloud computing<br/>Bayesian statistics<br/>machine learning <br/><br/>**Your development**<br/>We have substantial expertise in data science, focusing on a wide range of application areas. This includes:<br/>healthcare<br/>transport<br/>cybersecurity<br/>smart cities<br/>manufacturing<br/><br/>We are home to the UK’s National Innovation Centre for Data (NICD). We are also a partner of the Alan Turing Institute, the national institute for data science and artificial intelligence. All our academic staff involved in teaching data science modules have international reputations for their contributions to the field. Many of them have extensive experience as practitioners in industry as well as work in academia.<br/><br/>You will be encouraged to play a full part in the life of the School of Computing, including:<br/>taking advantage of dedicated computing and study facilities<br/>participating in seminars delivered by researchers and distinguished external speakers<br/><br/>**Delivery**<br/>The School of Computing and School of Mathematics, Statistics and Physics deliver the course. The course starts in mid-September. You will be taught in modern facilities in the newly-opened Urban Sciences Building. The course has three phases.<br/><br/>In phase one you’ll be introduced to core knowledge and skills in statistics and computer science. These modules are taught as an intensive block, with two modules taught concurrently for full-time students. Teaching is timetabled to accommodate participants from industry, working alongside full-time employment.<br/><br/>Phase two will present further advanced technical modules. You will be introduced to the aspects that underlie all areas of data science practice including:<br/>professionalism<br/>legislation<br/>ethics<br/><br/>This phase also includes a group project in collaboration with industry. Youll develop and evaluate a data science solution to a complex, real-world problem.<br/><br/>Phase three is an individual research and development project. Youll receive personal supervision in one of the School’s research labs in collaboration with industry or with your current employer.<br/><br/>Youll be assessed by a portfolio of practical work, accompanied by an oral interview. There will be no written examinations as part of the Data Science MSc.<br/><br/>If you’re a part time student, you have the flexibility to study over two years. The part time version of the course encourages participation of practitioners from industry. As a part time student you can:<br/>align your assessed work with the priorities of your job role<br/>carry out your individual project in your place of work (as long as the supervisory processes in place meet University standards)