The demand for data scientists in the UK has grown more than ten-fold in the past five years *. The amount of data in the world is growing exponentially. From analysing tyre performance to detecting problem gamblers, wherever data exists, there are opportunities to apply it.
City’s MSc Data Science programme covers the intersection of computer science and statistics, machine learning and practical applications. We explore areas such as visualisation because we believe that data science is about generating insight into data as well as its communication in practice.
The programme focuses on machine learning as the most exciting technology for data and we have learned from our own graduates that this is of high value when it comes to employment within the field. At City, we have excellent expertise in machine learning and the facilities students need to learn the technical aspects of data analysis. We also have a world-leading centre for data visualisation, where students get exposed to the latest developments on presenting and communicating their results – a highly sought after skill.
*From IT jobs watch
“What is happening with data? Who is in control of it? Who owns data? I don’t think we have even started to fully understand these questions. This is part of what we cover on this programme: the bottom line is with great power comes great responsibility.”
Programme director, Dr Tillman Weyde
This programme is for students who have a numerate first degree or can demonstrate numerate skills. Students are often at the early stages of their careers in diverse professions including economics, statistics and computer science.
Students will have a curiosity about data, and will want to learn new techniques to boost their career and be part of exciting current industry developments. The MSc in Data Science includes some complex programming tasks because of the applied nature of the course, so many students have a mathematics or statistics background and enjoy working with algorithms.
BCS (applied for): CITP FL (full) and CEng/CSci (partial)
Applicants should hold an upper second class honours degree or the equivalent from an international institution in computing, engineering, physics or mathematics, or in business, economics,psychology or health , with a demonstrable mathematical aptitude, or a lower second class honours degree (or international equivalent) with a demonstrable mathematical aptitude and relevant work experience.
INTO Postgraduate preparation Programmes
If you do not qualify for direct entry, our partner INTO City University London offers academic preparation programmes which focus on the skills you need. Successful completion of the Graduate Diploma in Science and Engineering at INTO City University London means guaranteed progression to this degree.
For those students whose first language is not English, the following qualification is also required:
Please note that due to changes in the UKVI 's list of SELT s we are no longer able to accept TOEFL as evidence of English language for students who require a CAS as of April 2014.
If you need to improve your English language skills before you enter this course, our partner, INTO City University London offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for entry to this degree. Please click the links below for more information.
If you are not from the European Economic Area / Switzerland and you are coming to study in the UK, you may need to apply for a visa or entry clearance to come to the UK to study.
The way that you apply may vary depending on the length of your course. There are different rules for:
If you require a Tier 4 student visa to study in the UK, you cannot undertake any City courses on a part-time basis.
For more information see our main Visa page.
Tillman Weyde is a Senior Lecturer in the Department of Computing. Before that he was a researcher and coordinator of the MUSITECH project at the Research Department of Music and Media Technology at the University of Osnabrück. Tillman was a consultant to the NEUMES project at Harvard University and he is a member of the MPEG Ad-Hoc-Group on Symbolic Music Representation. He currently works on Semantic Web representations for music, methods for automatic music analysis, audio-based similarity and recommendation and general applications of audio processing and machine learning in industry and science.
We offer a free language course for City University London students.
Tutorials are used to help you develop skills in applying the concepts covered in the lectures, normally in practical problem solving contexts.
Laboratory sessions serve a similar purpose as the tutorials but their strategy is to demonstrate application of concepts and techniques through the use of state-of-the-art software development tools and environments.
In addition to lecture, laboratory and tutorial support, each student will be assigned an academic member of staff as a personal tutor. The course is also supported by the City University online learning environment Moodle, which will contain resources for each of the modules. These include materials such as lecture notes and lab materials, coursework feedback and model answers, as well as an interactive discussion forum.
You are expected to undertake independent study and coursework for each module, amounting approximately to 120 hours per module for a full-time student. Modules are mainly assessed through a combination of written examination and coursework assessments normally containing theoretical and practical questions requiring the analysis and exemplifying of data science methods and techniques. The assessment criteria will reflect the learning outcomes of the modules and the programme as a whole. Assessment feedback will be provided following the City University London Assessment and Feedback Policy.
The individual project is a substantial task that develops a research related topic and is performed under the supervision of an academic member of staff. During the project, you will be given an opportunity to solve a real problem using big data from industry, academia or government, e.g. collecting and processing real data, designing and implementing Big Data methods and tools, applying and evaluating big data techniques to solve a real problem. The assessment of individual projects is based on the submission of a substantial MSc project report.
The course covers the study and integration of advanced methods and techniques from data analysis and machine learning, data visualisation and visual analytics, high-performance, parallel and distributed computing, knowledge representation and reasoning, neural computation, signal processing, data management and information retrieval. It will enable you to specialise in an application area of data science, from health to retail, and engage with researchers and industrial partners to develop your knowledge and skills in each of the above areas.
During the project, you will solve a real-world problem using big data from industry, academia or government, e.g. collecting and processing real data, designing and implementing Big Data methods and tools, applying and evaluating big data techniques to solve a real problem in the areas of health, security, business, transport, energy, online education, retail and the creative industries.