Course summary
This unique apprenticeship programme aims to prepare you with the knowledge and values you need for a technical career as a data scientist. During your course you’ll gain leadership skills to enable you to manage complex data science projects to help progress your career. The benefits of this course are many and varied. You will build knowledge of the data science field, explore data visualisation and focus on machine learning, as this is of high value to employers within the sector. You will also have access to our world-leading centre for data visualisation where you’ll be exposed to the latest developments on presenting and communicating your data analysis – a highly sought-after skill. This course is an apprenticeship, and to apply you must be employed at a company that supports your enrolment on the scheme, or you’ve received a job offer with a firm that wishes to employ you as an apprentice. You should have a curiosity and interest in data with a strong desire to learn new techniques to boost your career. Your course includes some complex programming tasks so it’s likely you’ll come from a professional background that requires good numeracy and enjoy working with algorithms.
Modules
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). *Due to the COVID-19 pandemic, sessions may be spread over 2 days. 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)
- Executive Development (15 credits)
- Project Management (15 credits)
Assessment method
You will learn through a series of lectures, tutorials and practical sessions helping you to increase your specialist knowledge and autonomy. 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.
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 a lower class degree with 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 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.
Fees and funding
Tuition fees
| England | £21000 | Whole course |
| Northern Ireland | £21000 | Whole course |
| Scotland | £21000 | Whole course |
| Wales | £21000 | 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 (City St George's, University of London)
Northampton Square
City of London
EC1V 0HB