Recognition and accreditation of prior learning for Data Science (MSc, PGDip, PGCert)

Data Science and specialisms (MSc, PGDip, PGCert)

If you already hold qualifications with similar learning outcomes to Data Science modules, you can apply for your prior learning to be recognised and accredited. If this is successful, you will not need to study those modules to complete your award.

How it works / How to apply

New students

When submitting your online application, you can apply for Recognition of Prior Learning (RPL). The rules for recognition of prior learning are described in the Data Science Programme Regulations.

MSc Data Science and specialisms - you may apply for recognition of prior learning (RPL) mapped against modules up to a total of 120 UK credits

PGDip - you may apply for recognition of prior learning mapped against modules up to a total of 60 UK credits.

PGCert - you may apply for recognition of prior learning mapped against modules up to a total of 30 UK credits.

*Recognition of prior learning will not be considered for the Final Project

You can apply for automatic or discretionary recognition of your prior learning.

Automatic RPL

There are certain qualifications which we will recognise and accredit automatically. Automatic RPL applications are free of charge.

The courses that comprise these qualifications have already been assessed and are considered by the University to have a similar syllabus or similar learning outcomes to modules on the Data Science programme.

If you have passed the correct subjects and satisfy the conditions we specify, we will recognise prior learning as detailed in the list below; however, you must still make a formal application and provide the necessary evidence. Please note, we are unable to fully consider your application until we have received the necessary documentary evidence.

Discretionary RPL

You may apply for recognition and accreditation of prior learning on a discretionary basis. Discretionary RPL is considered on a case by case basis. You will need to satisfy the University that you have covered a similar syllabus, as part of a previous qualification, at the same level, depth and breadth.

A formal application is required and an RPL application fee is payable. The RPL application fee is non-refundable, even if your prior learning is not recognised.

Please note that we are unable to give advice on discretionary RPL until we have received the completed RPL application form, the fee payable and any documentary evidence we have requested in support of the application.

Automatic Recognition of Prior Learning

Mapping against specific qualifications

Recognition of Prior Learning is the recognition of previously acquired learning which can be mapped against particular learning outcomes of modules within a programme.

If prior learning is recognised for a specific module, you will not be required to study or be assessed in that module and you will be considered to have achieved the learning outcomes for the purposes of the award.

Your transcript will indicate the credit value for any module where prior learning is recognised and accredited. The process is referred to as accreditation of prior learning. The mark obtained for the earlier qualification will not be carried forward to your record and will not contribute towards your award.

The University reserves the right not to recognise prior learning if the qualification of the respective professional body or institution changes after the publication of this list.

We will not recognise or accredit prior learning for a module later than 14 days after the module start date. You will be deemed to have started a module once you have been given access to the learning materials on the VLE.

Further information on rules regarding RPL is covered in Section 3 of the Programme Regulations and Section 3 of the General Regulations. See the Regulations section.

This table is only for students registered on one of the Data Science programmes:

Awarding body

Qualification

Data Science modules for which prior learning is recognised

Singapore Polytechnic

Specialist Diploma in Data Science (AI)

Specialist Diploma in Data Science (Big Data and Streaming Analytics)

Specialist Diploma in Data Science (Data Analytics)

Specialist Diploma in Data Science (Predictive Analytics)

Providing that for each qualification listed above, the following module has been successfully completed:

IT8701 Programming for Data Science

FHEQ Level 7: 15 credit modules:
Data programming in Python in DSM020]

Singapore Polytechnic

Specialist Diploma in Data Science (AI)

Specialist Diploma in Data Science (Big Data and Streaming Analytics)

Specialist Diploma in Data Science (Data Analytics)

Specialist Diploma in Data Science (Predictive Analytics

Providing that for each qualification listed above, one of the following modules has been successfully completed:

IT8701 Programming for Data Science

MS9001 Statistics for Data Science

IT8302 Applied Machine Learning

FHEQ Level 7: 15 credit modules:
Data Visualisation [DSM050]