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  1. Postgraduate
  2. 2019
Study at City

MSc Data Science

Entry Year:
Data science is an emerging new area of science. With City’s MSc in Data Science you can develop the skills and knowledge to analyse data in many forms and communicate insights.

Key information

Start date

September 2019

Academic year dates


Full-time: 12 months (15 months with option of three month internship)
Part-time: 24 months


Full-time: £9,750

Part-time: £4,875 per year *


Full-time: £17,500

Part-time: £8,750 per year *


Northampton Square

Who is it for?

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.


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.”

Senior lecturer, Dr Tillman Weyde


BCS Accredited Degree

Accredited by BCS, The Chartered Institute for IT for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional and partially meeting the academic requirement for registration as a Chartered Engineer.

Requirements and how to apply

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.

English requirements

For overseas students whose first language is not English, the following qualification is required:

  • IELTS: 6.5 (minimum of 6.0 in all four components)

English language programmes

Don't meet the English language requirements? INTO City, University of London offers English language programmes to help prepare you for study at university. These intensive and flexible courses are designed to improve your English ability for entry to degree courses.

Visa requirements

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:

  • Students on courses of more than six months
  • Students on courses of less than six months
  • Students on a pre-sessional English language course.

For more information see our main Visa page.

How to apply

We are no longer accepting applications to the MSc Data Science programme for 2019/20 entry.

Information about the programme for entry in 2020/21 will be published in September 2019.

Contact information for the Postgraduate Team

Tel: +44 (0)20 7040 0248

Postgraduate Courses Office, A302
School of Mathematics, Computer Science & Engineering
City, University of London
Northampton Square

course of its kind in London and one of the first Data Science MSc programmes in the UK
the median advertised salary for permanent jobs as a Data Scientist in the UK according to IT Jobs Watch
During your course

More about fees

Fees in each subsequent year of study (where applicable) will be subject to an annual increase of 2%. We will confirm any change to the annual tuition fee to you in writing prior to you commencing each subsequent year of study (where applicable).

If a student leaves City after commencing but before completing their course, City reserves the right to charge the student the tuition/course fee for the full academic year (or full course for capacity limited post-graduate courses - up to a maximum of 2 years fees) in question. The student may be charged the full fee for that year or course as applicable unless the student is able to present justification that exceptional and unforeseeable reasons for their withdrawal exist.

How to pay

City has introduced an instalment payment scheme which is available to certain categories of students, including taught postgraduate students. For students following the normal academic year, the annual fee may be paid in two equal instalments: the first on registering, the second on 31st January. If you wish to pay your fees by instalment you must pay the first instalment at or before registration, by cheque or credit/debit card. You must also supply your bank details or credit card details for payment of your second instalment which will be deducted automatically from your bank or credit card account on 31st January.

From one of our academics

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.


MSc Data Science students can participate in our professional internships programme, which is supported by the Professional Liaison Unit. This will enable you to undertake your MSc project in an industrial or research internship over an extended period compared to regular projects. For example, the individual project can be carried out as a 6-month internship in one of the companies with which City has a long-standing relationship and history of collaboration in the big data and data science area:

  • NHS
  • Facebook UK
  • Amazon UK
  • BBC
  • British Library
  • Octo Telematics
  • Selex Galileo
  • Microsoft
  • AT&T
  • IBM
  • Google
  • Unilever
  • Deloitte UK
  • Tableau Software
  • Oracle
  • Cancer Research UK

Examples of company placements internships taken by our Data Science students in the recent past include: Google, SagePay, Reward, Black Swan.

Academic facilities

The School's computer science laboratories are equipped with the latest up-to-date hardware and software. From Oracle’s leading commercial object-relational database server to PCs with state-of-the-art NVidia GPUs for computer graphics, you will have access to an array of tools to support your learning.

The MSc Data Science programme offers two (three by mid 2016) dedicated computer servers for the Big Data module, which you can also use for your final project to analyse large data sets. We give you the opportunity to undertake training in MATLAB, the most popular numerical and technical programming environment, while you study.

As part of the University of London you can also become a member of Senate House Library for free with your student ID card.


We offer a variety of accommodation options and support services for postgraduate students.

Read more about our postgraduate halls.

Our Accommodation Service can also help you find private accommodation.

Find out more about private accommodation.

Learn a language for free

We offer a free language course for City, University of London students.

Find out how to apply


Course timetables are normally available from July and can be accessed from our timetabling pages. These pages also provide timetables for the current academic year, though this information should be viewed as indicative and details may vary from year to year.

View academic timetables

Please note that all academic timetables are subject to change.

Student support

We offer an extensive support network during your time here at City, University of London – from Learning Support (including disability support) and counselling to financial and career advice – leaving you free to enjoy every opportunity campus life has to offer.

Find out more about the different types of student support available.

Course content and assessment

Teaching and learning

The teaching and learning methods we use mean that students’ specialist knowledge and autonomy increase as they progress through each module. Active researchers guide your progress in the areas of machine learning, data visualization, and high-performance computing, which culminates with an individual project. This is an original piece of research conducted with academic supervision, but largely independently and, where appropriate, in collaboration with industrial partners.

Taught modules are delivered through a series of 20 hours of lectures and 10 hours of tutorials/laboratory sessions. Lectures are normally used to:

  • present and exemplify the concepts underpinning a particular subject
  • highlight the most significant aspects of the syllabus
  • indicate additional topics and resources for private study.

Tutorials help you develop the skills to apply the concepts we have covered in the lectures. We normally achieve this through practical problem solving contexts.

Laboratory sessions give you the opportunity to apply concepts and techniques using state-of-the-art software, environments and development tools.

In addition to lectures, laboratory sessions and tutorial support, you also have access to a personal tutor. This is an academic member of staff from whom you can gain learning support throughout your degree. In addition, City’s online learning environment Moodle contains resources for each of the modules from lecture notes and lab materials, to coursework feedback, model answers, and an interactive discussion forum.

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.


Full-time students attend all taught modules during Term 1 (Oct-Dec) and Term 2 (Jan-Mar), and then complete their Project over 3 months (Jul-Sep), within the 12-month period of the degree (15-months for the internship route). This assumes they pass all the modules.

Students will usually need to attend three or four days a week. Teaching is generally during day (09:00 to 18:00), but some elective modules may be taught in the evening. Full time students may take an internship route, in which they are given an extra three months for an internship-based Project.


Part-time students take half the modules during Term 1 (Oct-Dec) and Term 2 (Jan-Mar) in the first year, the remaining modules during Term 3 (Oct-Dec) and Term 4 (Jan-Mar) in the second year, and then complete their Project over 6 months (Jul-Dec), within the 27-month period of the degree. This assumes they pass all the modules.

Students will need to attend one or two days a week. In terms 1 and 2 (first year) we do our best to schedule all the teaching on one day of the week (this has always been the case). In term 3, there may be an evening option for one the core modules. Teaching is generally during day (09:00 to 18:00), but some elective modules may be taught in the evening.


The programme is made up of six core modules, two elective modules and a final project. All the electives are studied in the second term. The third term is reserved for the project, where students solve a real-world problem using large datasets from industry, academia or government.

Modules include hands-on, lab-based tutorials and coursework. We teach the use of data science tools and technologies such as Python, Apache Spark and Matlab. You can pursue a practical MSc project in an application area of your choice.

If we have insufficient number of students interested in an elective module, this may not be offered. In rare cases, one or two elective modules may not run due to low interest or unanticipated changes in timetabling, including scheduling clashes, room and staff availability. If an elective module will not run, we will advise you at the beginning of every academic term or as soon as possible, and help you choose an alternative module.

Course content

Data science is the area of study concerned with the extraction of insight from large collections of data.

The course covers the study, integration and application 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 gives you the opportunity to specialise so, once you graduate, you can apply data science to any sector from health to retail. By engaging with researchers and industrial partners during the programme, you can develop your knowledge and skills within a real-world context in each of the above areas.

Core modules

Principles of Data Science (15 credits)

  • Understand the foundations of the data science process, methods and techniques
  • Represent and organise knowledge about large heterogeneous data collections
  • Use mathematical models and tools for large-scale data analysis and reasoning
  • Critically evaluate the choice of data science techniques and tools for particular scenarios

Machine Learning (15 credits)

  • Understand the workings of important data science algorithms for learning under uncertainty
  • Rationally exploit both statistical and machine learning approaches in applications
  • Rigorously assess the validity of inferences and generalisations
  • Critically evaluate the choice of algorithms for specific scenarios and requirements

Big Data (15 credits)

  • Understand the theory and techniques for data acquisition, cleansing, and aggregation
  • Identify and understand the principles and functionalities of big data programming models and tools
  • Acquire, process and manage large heterogeneous data collections
  • Develop algorithms and systems for information and knowledge extraction from large data collections

Neural Computing (15 credits)

  • Understand how to use neural computing and deep learning in an application domain
  • Select and apply supervised, unsupervised and hybrid neural networks to different problems and data types
  • Critically evaluate a range of neural systems in comparison with a number of learning techniques
  • Design and implement neural network models; apply them and evaluate their performance

Visual Analytics (15 credits)

  • Learn the principles and rules underlying the design of visual data representations and human-computer interactions
  • Understand, adapt and apply representative visual analytics methods and systems for diverse types of data and problems
  • Analyse and evaluate the structure and properties of data to select or devise appropriate methods for data exploration
  • Combine visualization, interactive techniques, and computational processing to develop practical data analysis for problem solving

Research Methods and Professional Issues (15 credits)

  • Understand and apply research methodologies such as inductive and deductive reasoning, explanation and prediction
  • Recognise and apply the scientific method and a range of secondary data sources when performing a research task
  • Communicate effectively with individuals and groups using a range of media
  • Evaluate the legal, ethical and professional dimensions of typical information professions and information industry practices.

Elective modules:

  • 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)
  • Deep Learning: Optimization (15 credits)

Individual Project (60 credits)

  • 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 data techniques to solve a real problem.
  • Carry out a piece of research conducted largely independently with academic supervision and, where appropriate, in collaboration with our industrial partners; the MSc project can be carried out as a six month internship or placement in a company.

MATLAB training

In partnership with MATLAB's MathWorks, postgraduate data science students can take the MathWorks online training, free of charge, including three self-placed online courses:

  • MATLAB Fundamentals
  • MATLAB Programming Techniques
  • MATLAB for Data Processing and Visualisation

MathWorks certification programme

Upon successful completion of the online training in MATLAB, should you wish to pursue the MathWorks MATLAB certification, you will be competent to pass the MathWorksCertified MATLAB Associate Exam.

The programme specification contains more information on how the course is organised, the requirements for progression for each part and credits required for awards.

Recommended reading

We recommend students start learning Python as it is the programming language they will use at the start of the programme.

  • Sathu Tarimela
    Student profile
    My passion to pursue a master's in IT has driven me to explore the best universities in the world, offering versatile world class computer science programs. City offered all the components I was expecting from a global university..
    Data Science
After you graduate

Career prospects

From health to retail, and from the IT industry to government, the Data Science MSc will prepare you for a successful career as a data scientist. You will graduate with specialist skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation, which are in high demand.

City's unique internships, our emphasis on machine learning and visual analytics, together with our links with the industry and Tech City, should help you gain employment as a specialist in data analysis and visualization. Graduates starting a new business can benefit from City's London City Incubator and City's links with Tech City, providing support for start-up businesses.

Career & Skills Development Service at City, University of London

After successful completion of the course you may wish to consider a PhD degree in Computing.

Data Bites

City’s Department of Computer Science hosts the Data Bites series of talks on data topics. The Data Bites series regularly features employers in the data science market presenting their companies and job opportunities.

We are no longer accepting applications to the MSc Data Science programme for 2019/20 entry.

Information about the programme for entry in 2020/21 will be published in September 2019.

Contact information for the Postgraduate Team

Tel: +44 (0)20 7040 0248

Postgraduate Courses Office, A302
School of Mathematics, Computer Science & Engineering
City, University of London
Northampton Square

Contact details

Programmes Office (room A302)

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