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  1. Data Science
    1. 2016
    2. 2017

Data Science

MSc |
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

Autumn 2017

Duration

Full-time: one year
Part-time: up to 28 months

UK/EU

Full-time: £9,000

Part-time: £4,500 per year

Non-EU

Full-time: £15,000

Part-time: £7,500 per year

Who is it for?

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

Objectives

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.

Accreditation

British Computer Society logo

BCS (applied for): CITP FL (full) and CEng/CSci (partial)

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

Other Suitable Qualifications

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.

English requirements

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

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

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.

INTO English Language Programmes

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.

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.

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.

How to apply

Apply for MSc Data Science

You will be able to attach electronic copies of your supporting documents. Please ask your referees to submit your confidential reference letters in hard copy to the address below. You will be required to send any supporting documents you do not attach when applying online to the same address.

Postal applications and supporting documents

Alternatively, to receive an application pack in the post please contact the Programmes Office:

Tel: +44 (0) 20 7040 0248
Email: smcsepg@city.ac.uk

We encourage online applications. However, we ask that you also send two references and your original transcript by post as soon as possible after submitting your online application to:

Postgraduate Courses Offices
School of Mathematics, Computer Science & Engineering
City University London
Northampton Square
London
EC1V 0HB

1st
course of its kind in London and one of the first Data Science MSc programmes in the UK
£60,000
the median advertised salary for permanent jobs as a Data Scientist in the UK according to IT Jobs Watch

Funding

Explore up-to-date information about funding options, available financial support and typical living costs.

More about funding

Future Finance Loans

Future Finance offers students loans of between £2,500 and £40,000 to help cover tuition fees and living expenses. All students and courses are considered. All loans are subject to credit checks and approval for further details please visit the City Finance website.

Scholarships

From our Course Director

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.

Placements

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

Learn a language for free

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

Find out how to apply

Teaching and learning

The teaching and learning methods used are such that the levels of both specialisation of content and autonomy of learning increase as you progress through each module and the programme. This progression will be guided by active researchers in the areas of Machine Learning, Data Visualization, and High-Performance Computing, culminating with an individual project containing an original piece of research conducted largely independently with appropriate academic supervision and, where appropriate, in collaboration with industrial partners.

The standard format is that 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 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.

Please note, course details are subject to change as part of the University approval process.

Modules

The MSc in Data Science will prepare you for a successful career as a data scientist. Data Science is the area of work concerned with the extraction of insight from large collections of data. The MSc in Data Science will develop your specialist skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation, which are in high demand.

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.

Modules will include hands-on, lab-based tutorials and coursework, and the use of Data Science tools and technologies such as Python, Matlab, Apache Spark, JavaScript and GPU programming which will equip you very well to pursue a practical MSc project in an application area of your choice.

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.

The MSc in Data Science aims to prepare you with the knowledge, skills and values needed for a career as a Data Scientist by:

  • equipping you with the breadth of knowledge, skills and techniques needed,
  • developing your knowledge in specialised and advanced topics in data science
  • enabling you to work with and learn from active researchers in machine learning, high-performance computing and data visualization,
  • enabling you to critically evaluate the technical, social and management dimensions of data-intensive systems and technologies

The course is composed of six core modules, a choice of two electives and an individual MSc project.

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 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)
  • Software agents (15 credits)

MSc Project

  • 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, MSc 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 a 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.

Career prospects

You can expect to achieve employment as a Data Scientist in a range of businesses, from health to retail, the IT industry or in government.

City's unique internships, and the emphasis of this programme on machine learning and visual analytics, with our links with many industrial partners and Tech City, should particularly enable you to gain appointments as a specialist in data analysis and visualization in the health, business, security, transport and energy sectors, retail and the creative industries, in a host of organisations. 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 London

This course will enable you to...

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

Data Bites

City University's Computer Science Department 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.


Apply for MSc Data Science

You will be able to attach electronic copies of your supporting documents. Please ask your referees to submit your confidential reference letters in hard copy to the address below. You will be required to send any supporting documents you do not attach when applying online to the same address.

Postal applications and supporting documents

Alternatively, to receive an application pack in the post please contact the Programmes Office:

Tel: +44 (0) 20 7040 0248
Email: smcsepg@city.ac.uk

We encourage online applications. However, we ask that you also send two references and your original transcript by post as soon as possible after submitting your online application to:

Postgraduate Courses Offices
School of Mathematics, Computer Science & Engineering
City University London
Northampton Square
London
EC1V 0HB

Contact details

Programmes Office (room A302)

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