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City, University of London

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City, University of London

Data Science

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.

Entry requirements

MSc: 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 and basic programming experience, or a lower second-class honours degree (or international equivalent) with a demonstrable mathematical aptitude and relevant work experience.

Other suitable qualifications
If you do not qualify for direct entry, you may wish to follow a Graduate Diploma pathway to the programme through one of our partners.

INTO City, University of London
Don't meet the entry requirements? INTO City, University of London offers a range of academic and English language programmes to help prepare you for study at City, University of London. You'll learn from experienced teachers in a dedicated international study centre.

These programmes are designed for international students who do not meet the required academic and English language requirements for direct entry. To prepare for this degree course, learn more about the Graduate Diploma in Informatics - Science and Engineering.

Course modules

Core modules:
Principles of data science (15 credits);
Machine learning (15 credits);
Big Data (15 credits);
Neural computing (15 credits);
Visual analytics (15 credits); and
Research methods and professional issues (15 credits).

Elective modules:
Advanced programming: concurrency (15 credits);
Readings in computer science (15 credits);
Advanced databases (15 credits);
Information retrieval (15 credits);
Data visualisation (15 credits);
Digital signal processing and audio programming (15 credits);
Cloud computing (15 credits);
Computer vision (15 credits); and
Software agents (15 credits).

Individual Project (60 credits).
MATLAB training.

Assessment methods

We expect you to study independently and complete coursework for each module. This should amount to approximately 120 hours per module if you are studying full time. Each module is assessed through a combination of written examination and coursework, where you will need to answer theoretical and practical questions to demonstrate that you can analyse and apply data science methods and techniques.

The individual project is a substantial task. It is your opportunity to develop a research-related topic under the supervision of an academic member of staff. This is the moment when you can apply what you have learnt to solve a real-world problem using large datasets from industry, academia or government and use your knowledge of collecting and processing real data, designing and implementing big data methods and applying and evaluating data analysis, visualisation and prediction techniques. At the end of the project you submit a substantial MSc project report, which becomes the mode of assessment for this part of the programme.


Qualification Study mode Start month Fee Fee locale Course duration
MSc Part-time September 2018 8,500 (Year 1) International 28 28 Months
MSc Part-time September 2018 4,750 (Year 1) Home/EU 28 28 Months
MSc Full-time September 2018 17,000 (Whole course) International 12 12 Months
MSc Full-time September 2018 9,500 (Whole course) Home/EU 12 12 Months

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