THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2025/2026
- ARCHIVE as at 1 September 2025

Timetable information in the Course Catalogue may be subject to change.
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Degree Programme Table: Data Science Technologies (MSc) (PTMSCDSTEC1P)

Jump to: Year 1, Year 2, Year 3

Year 1 Academic year: 2025/26, Starting in: September

Notes:
This is the Degree Programme Table (DPT) for the part-time version of the Data Science Technologies programme.

If you need any academic advice about your course choices, please email your Programme Director, Dr Adam Carter: Bayes.PGTDirector@ed.ac.uk

If you have any questions about the requirements for your programme, please email your Student Adviser, Cat Andrade-Robertsen: Bayes.StudentServices@ed.ac.uk

Compulsory courses

You must take these courses
Practical Introduction to Data Science

Must be taken during Full Year

EPCD11005 20 credits
Introductory Probability and Statistics

Must be taken during Semester 1

MATH11203 10 credits
Programming Skills

Must be taken during Semester 1

EPCD11018 10 credits

Course options

DST (PT) options

Select exactly 20 credits of the following courses
Notes:
You are recommended to balance your credits evenly across each semester.
Leading Technology and Innovation in Organisations
CMSE11495 10 credits
Innovation-driven Entrepreneurship-Data Science, Technology and Innovation
CMSE11515 10 credits
Practical Introduction to High Performance Computing
EPCD11001 20 credits
Message-Passing Programming
EPCD11002 10 credits
Threaded Programming
EPCD11003 10 credits
Software Development
EPCD11017 10 credits
Data Ethics in Health and Social Care
HEIN11059 10 credits
Data Types and Structures in Python and R
HEIN11068 10 credits
Natural Language Processing (NLP) in Health and Social Care
HEIN11085 10 credits
Introduction to Bioinformatics
PGBI11137 10 credits
Data Science for Manufacturing (Bayes)
PGEE11234 10 credits
Technologies of Civic Participation
PGSP11390 10 credits
Engaging with Digital Research
PGSP11401 10 credits

Year 2 Academic year: 2025/26, Starting in: September

Notes:
If you need any academic advice about your course choices, please email your Programme Director, Dr Adam Carter: Bayes.PGTDirector@ed.ac.uk

If you have any questions about the requirements for your programme, please email your Student Adviser, Cat Andrade-Robertsen: Bayes.StudentServices@ed.ac.uk

Compulsory courses

You must take these courses
Applied Machine Learning

Must be taken during Semester 2

INFD11032 20 credits

Course options

Group A

Select exactly 40 credits in this group.

Informatics Project Proposal 20 (Distance Learning)

Select exactly 20 credits of the following courses
Selected courses must be taken during Semester 2
Informatics Project Proposal 20 (Distance Learning)
INFD11031 20 credits
AND

DST (PT) options

Select exactly 20 credits of the following courses
Notes:
You are recommended to balance your credits evenly across each semester.
Leading Technology and Innovation in Organisations
CMSE11495 10 credits
Innovation-driven Entrepreneurship-Data Science, Technology and Innovation
CMSE11515 10 credits
Practical Introduction to High Performance Computing
EPCD11001 20 credits
Message-Passing Programming
EPCD11002 10 credits
Threaded Programming
EPCD11003 10 credits
Software Development
EPCD11017 10 credits
Data Ethics in Health and Social Care
HEIN11059 10 credits
Data Types and Structures in Python and R
HEIN11068 10 credits
Natural Language Processing (NLP) in Health and Social Care
HEIN11085 10 credits
Introduction to Bioinformatics
PGBI11137 10 credits
Data Science for Manufacturing (Bayes)
PGEE11234 10 credits
Technologies of Civic Participation
PGSP11390 10 credits
Engaging with Digital Research
PGSP11401 10 credits
OR

Group B

Select exactly 40 credits in this group.

Project Preparation (DSTI)

Select exactly 10 credits of the following courses
Selected courses must be taken during Semester 2
Project Preparation (DSTI)
EPCD11014 10 credits
AND

DST (PT) options

Select exactly 30 credits of the following courses
Notes:
You are recommended to balance your credits evenly across each semester.
Leading Technology and Innovation in Organisations
CMSE11495 10 credits
Innovation-driven Entrepreneurship-Data Science, Technology and Innovation
CMSE11515 10 credits
Practical Introduction to High Performance Computing
EPCD11001 20 credits
Message-Passing Programming
EPCD11002 10 credits
Threaded Programming
EPCD11003 10 credits
Software Development
EPCD11017 10 credits
Data Ethics in Health and Social Care
HEIN11059 10 credits
Data Types and Structures in Python and R
HEIN11068 10 credits
Natural Language Processing (NLP) in Health and Social Care
HEIN11085 10 credits
Introduction to Bioinformatics
PGBI11137 10 credits
Data Science for Manufacturing (Bayes)
PGEE11234 10 credits
Technologies of Civic Participation
PGSP11390 10 credits
Engaging with Digital Research
PGSP11401 10 credits

Year 3 Academic year: 2025/26, Starting in: September

Notes:
If you took Informatics Project Proposal in Year 2, you must enrol on INFD11003 Data Science, Technology and Innovation; if you took EPCD11014 Project Preparation (DSTI) in Year 2, you must enrol on EPCD11019 Dissertation (DSTI - EPCC).

There are no compulsory courses in this year of this programme.

Course options

Data Science, Technology and Innovation Dissertation

Select exactly 60 credits of the following courses
Selected courses must be taken during Full Year
Data Science, Technology and Innovation Dissertation
INFD11003 60 credits
OR

Dissertation (DSTI - EPCC)

Select exactly 60 credits of the following courses
Selected courses must be taken during Full Year
Dissertation (DSTI - EPCC)
EPCD11019 60 credits
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