Course summary
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. Objectives 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.
Modules
Alongside modules we also recommend students start learning Python as it is the programming language they will use at the start of the programme. Full-time Full-time students attend all taught modules during Term 1 and 2, three to four days a week. Teaching is generally during the day (09:00 to 18:00), but some elective modules may be taught in the evening. When you have passed all the modules, you will proceed to your individual project in Term 3. Part-time Part-time students take half the modules during Term 1 and 2 in the first year, and half in the second year. You will need to attend one or two days a week. Teaching is generally during the day (09:00 to 18:00), but some elective modules may be taught in the evening. When you have passed all the modules, you will proceed to your individual project over 6 months (Jul-Dec), within the 27-month period of the degree. Core Modules
- Principles of Data Science (15 credits)
- Machine Learning (15 credits)
- Big Data (15 credits)
- Visual Analytics (15 credits)
- Neural Computing (15 credits)
- Research Methods and Professional Issues (15 credits)
- Advanced Databases (15 credits)
- Information Retrieval (15 credits)
- Data Visualization (15 credits)
- Digital Signal Processing and Audio Programming (15 credits)
- Deep Reinforcement Learning (15 credits)
- Computer Vision (15 credits)
- Semantic Web Technologies and Knowledge Graphs (15 credits)
- Natural Language Processing (15 credits)
Assessment method
Typically, the assessment methods include a combination of written examination and coursework. The assessment of certain modules is based on coursework only, as detailed in each module’s specification. The written examinations will contain theoretical questions, including small essays and mathematical aspects, and practical questions requiring the analysis and exemplifying of data science methods and techniques.
Entry requirements
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. Kaplan International College London City works in partnership with Kaplan International College (KIC) London to provide preparatory courses for international students. Pre Masters courses at KIC London offer comprehensive support to students wishing to complete their postgraduate study at City. Progression to this degree is guaranteed if you complete the KIC London Pre-Masters course at the required level.
Fees and funding
Tuition fees
| EU | £22940 | Whole course |
| International | £22940 | Whole course |
| England | £11470 | Whole course |
| Scotland | £11470 | Whole course |
| Wales | £11470 | Whole course |
| Northern Ireland | £11470 | Whole course |
Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .
Additional fee information
No additional fees or cost information has been supplied for this course, please contact the provider directly.
Provider information
City, University of London
Northampton Square
City of London
EC1V 0HB