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  1. Postgraduate
    1. 2020
Study at City

MSc Data Science Apprenticeship

Entry Year:
Data science is an exciting new field with high industry demand. With City’s Apprenticeship MSc in Data Science you can develop the skills and knowledge to analyse data in many forms and communicate insights while gaining hands on skills at your company.

Key information

Start date

September 2020

Academic year dates


Part-time, day release (two modules per term, on one day per week) for 27 months (MSc element) plus additional time for end point assessment (up to 3 months).


Part-time: £21,000 (total cost)


Northampton Square

Who is it for?

This course is an apprenticeship course, and candidates must be based at a company that wishes them to be enrolled on the scheme, or have received an offer of employment within a company that wishes to employ them as an apprentice on this scheme. Apprentices are often at the early stages of their careers in diverse professions including economics, statistics and computer science.

Apprentices should have a curiosity about data, and a desire to learn new techniques to boost their career and be part of exciting current industry developments. The Data Science Apprenticeship MSc 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 Apprenticeship MSc in 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 to employers 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


Tech Partnership Degrees

Accredited by: Tech Partnerships Degrees

We will apply for BCS accreditation at their next visit.

Requirements and how to apply

Entry requirements

You should have a UK first or an upper second-class honours degree (or equivalent) in a subject area such as computing, mathematics, physics, engineering, information science, economics, or a related discipline with mathematical and computational content. We will also accept applicants with degrees in business, economics, psychology and health, if they demonstrate some statistical, mathematical and computer scripting aptitude, e.g. by referring to qualifications, courses and experience.

We may accept applicants with lower second-class degrees if they have relevant work experience, but this is at our discretion. We recommend your personal statement explains why you are interested in Data Science, points to relevant experience and indicates which particular aspects of our course that interest you.

You will require:

  • Level 2 qualification in English and Mathematics (usually GCSE)
  • Employment within an organisation that support your enrolment in the degree as part of your Digital and Technology Solutions Specialist(Data Analytics Specialist) training.

You must be a UK or EU national with the right to work in the UK. The Apprenticeship Levy covers roles in organisations in England.

Other suitable qualifications

Some applicants with extensive relevant work experience who do not fulfil the degree criteria may be accepted.

English requirements

If you are an applicant whose first language is not English, the following qualifications are required:

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

For information about the various English Language tests City accepts please see the English language requirements. Please note though that the scores listed there are equivalent to IELTS 5.5.

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.

Please note that due to changes in the UKVI’s list of SELTs we are no longer able to accept TOEFL as evidence of English language for students who require a CAS as of April 2014.

Visa requirements

International Students (EEA and Non EEA) coming to study in the UK, 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:

  • EEA nationals joining the programme in 2020 and EEA nationals joining from January 2021
  • 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

Thank you for having decided to apply to study a postgraduate course at the School of Mathematics, Computer Science and Engineering.

Apply now

MSc Data Science Apprenticeship September 2020 entry

You will be expected to submit the following:

  • For all applicants, please upload your degree certificate and transcript of marks from your first degree (if you do not have your final results at the time of making your application, please upload a provisional certificate/interim transcript of marks). A transcript is required in order to have your application processed.
  • If your first language is not English, or you require a Tier 4 visa to study in the UK, please upload a Proof of English Proficiency if you have already obtained it. A list of accepted qualifications can be found here.
  • Please upload a detailed personal statement outlining why you wish to study this specific course, at City, University of London, as well as explaining how your past studies have prepared you for this course and how it will help you to progress in your career. If you require a Tier 4 student visa to undertake a Master's programme in the UK, then please note that the quality of your personal statement will also be taken into account by the visa issuing authorities when deciding to grant you a student visa or not.
  • If you are applying for a Part-time course, or have relevant work experience relating to the degree you are applying for, please upload a copy of your current CV/resume.

Please note: Academic references are not required when you submit your application. However, the admissions tutor may request them at a later date to help make a decision on your application.

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

Based on the Data Science MSc, which was the first course of its kind in London and one of the first Data Science MSc programmes in the UK.
Market Leader
The Data Science MSc is the market leader, with over 600 applicants each year.
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

The course is funded under the apprenticeship levy scheme.

Larger employers: companies with an annual payroll of £3m+, and therefore liable to pay the apprenticeship levy from May 2017, will be able to fund up to 100% of a degree apprentice’s tuition costs from their levy contribution.

Smaller employers with over 50 employees: can claim 95% of the degree apprenticeship costs from the government, leaving them to pay only 5% themselves.

Smaller employers with fewer than 50 employees: can claim 100% of the degree apprenticeship costs from the government.

The Lord Mayor of London Scholarship

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.

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 Apprenticeship programme offers three 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 operate a Guarantee Scheme for first year undergraduates which means you will be offered place in one of City's affiliated Halls if you meet the Scheme's criteria.

Read more about our undergraduate 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.

Ask a student

Chat to our current students and read their blogs to gain an insight into studying at City and learn more about our undergraduate and postgraduate courses.

Course content and assessment

To make sure that you can begin or continue your studies with us during the COVID-19 pandemic, we have reviewed and adapted our courses to ensure a safe learning environment for our students and staff. We have modified the way some of our courses are delivered, with many programmes being made available online.

Contact us to find out more about how our programmes will be delivered.

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.

Taught modules are delivered through a series of 20 hours of lectures and 10 hours of tutorials/laboratory sessions. These are part-time, day release, with two module on each study day. 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.

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

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.

Online training resources

You may benefit from the following training resources provided to students on this course, free of charge:

MATLAB training

In partnership with MATLAB's MathWorks, postgraduate data science students can take the MathWorks online training. Upon successful completion of the online training in MATLAB you will be competent to pass the MathWorksCertified MATLAB Associate Exam.


DataCamp is an online training platform providing you with tutorials and challenges in Python and other data science technologies. *Please note this resource is subject to renewal on a six-monthly basis.


Modules are assessed through a combination of coursework and examination but some are coursework-only, where you will need to answer theoretical and practical questions to demonstrate that you can analyse and apply data science methods and techniques.

At the end of the individual project you submit a substantial MSc project report, which becomes the mode of assessment for this part of the programme.


Students take the course part-time, with half the modules during Term 1 and 2 in the first year, and half in the second year. You will need to attend one day a week. Teaching is during the day (09:00 to 18:00).

You then complete your individual project over 6 months (Jul-Dec), within the 27-month period of the degree. This assumes you pass all the modules. At the end of the MSc component you will complete an end point assessment, based on work-based assessments and project to fulfil the requirements of the level 7 apprenticeship qualification.


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
  • Data management and information retrieval.
  • Project Management
  • Executive Development

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.

Project Management (15 credits)

  • Take an active part in the development of an Information Systems (IS) project, as an IT practitioner, business or systems analyst or project development specialist
  • Understand the roles and skills needed at each stage of the project development process
  • Identify decisions to be made and their consequences with a focus on both theoretical and practical issues.

Executive development (15 credits)

  • Recognise and evaluate the responsibilities of professional roles with full awareness of the legal, ethical and professional issues that arise.
  • Formulate strategies for using negotiation and influencing in professional practice.
  • Communicate with impact and empathy, and use communication skills to develop personal impact and others.
  • Analyse management roles in complex situations, including commercial, legal, ethical and sustainability issues.
  • Reflect on own and others’ behaviour in order to improve performance and practice.
  • Identify and understand own development needs, and those of others, including issues of motivation, recognition and engagement.

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

  • Alice Ou
    Student profile
    City has strong connection with the industry and they have a lot of industrial events you can participate. I think it is really helpful for you to understand what you are up for after graduating from your degree.
    Data Science MSc
  • 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

City's unique Data Science Apprenticeship MSc will help you add value to your company through both your technical and leadership skills.

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.

Contact details

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