Data Science Technologies (Online Learning) MSc

How online learning works

This degree programme is taught entirely online. There is no need to come to the city or University campus.

All learning and teaching takes place within our virtual learning environment (VLE). Through the VLE, you can:

• access all your learning materials and study resources, including e-books and library resources
• interact with your tutors and classmates

An online degree from the University of Edinburgh is academically equivalent to an on-campus postgraduate degree and involves the same level of work overall. The qualification you get is of equal value. Your degree certificate will not mention that you studied the programme online.

Courses also use Teams chat rooms and online forums to facilitate asynchronous discussion.

Time commitment

This programme is designed to be fully flexible to fit around your schedule. You can study in your own time and access all your learning resources, such as reading lists, discussion forms and slides from anywhere in the world.

If there are live online sessions, you can watch a recording later in the virtual learning environment at a time convenient to you.

Typically, you will need to dedicate around 10 to 20 hours per week to your programme although managing this is up to you. This may also vary from course to course, and the time commitment may increase when assignments are due.

Equipment and software needs

To study this online programme, you will need access to:

• a computer or laptop
• the internet
• the latest version of a web browser

As an online student, you will have access to a range of software you can download to help you complete your coursework, including Microsoft Office 365.

IT support is available if you have technical difficulties.

IT support for online learners

Opportunities to attend in person

You can choose to graduate in person at our ceremony in Edinburgh.

Teaching

Online learning and teaching methods are determined by each individual course-owning unit.

Most courses use:

• Pre-recorded video materials
• Set practical exercises (some with access to computational resources)
• Recommended readings
• Regular synchronous (recorded) tutorials

Some courses involve group activities, but these will be arranged at times that are suitable for most participants. 

The variety of teaching methods means that you will learn in new and exciting ways.

Skills development

As you study with us, you will gain experience working and collaborating in groups.

You will be supported in developing your technical communication skills as well as transferable skills such as time management, organisation, and effective expectation management.

Assessment

Assessment methods vary depending on the standard practice of the course-owning School.

You will be assessed using a range of methods and styles including:

• Practical coursework
• Short essays
• Problem sets
• Presentation of group work

Most assessments are coursework-based, designed to be completed asynchronously over several weeks to provide flexibility. 

Some courses use some shorter, more regularly assessed practical activities timed to coincide with parts of the course.

A small number of courses use synchronous (online) exams or groupwork submissions.

The programme provides a pathway into the field of data science, introducing key programming skills and relevant mathematical concepts before demonstrating how these can be applied in data analytics and machine learning. 

You will complete a range of courses from across the University and learn how data science approaches are used across multiple domains.

Dissertation

After completing the taught component of the MSc, you will complete a dissertation project. 

This is a capstone research project that allows you to further explore an aspect of data science that interests you. You will work closely with a leading academic to investigate practical problems in your chosen topic.

Compulsory courses

The following courses are compulsory for both the full-time and part-time options:

• Practical Introduction to Data Science
• Programming Skills
• Introductory Probability and Statistics
• Applied Machine Learning

Option courses

• Data Ethics for Health and Social Care
• Data Science for Manufacturing
• Data Types and Structures in Python and R
• Data, Sport and Society
• Innovation-Driven Entrepreneurship
• Introduction to Bioinformatics
• Message-Passing Programming
• Leading Technology and Innovation in Organisations
• Natural Language Processing (NLP) for Health and Social Care
• Practical Introduction to High Performance Computing
• Software Development
• Technologies of Civic Participation
• Threaded Programming
• Understanding Data Visualisation

Disclaimer

Please note that these are indicative option courses. Some courses may not be offered every year, and full-time students may not have access to all courses on the list due to scheduling.

Academic facilities

Depending on the courses you select, you will have access to large, cutting-edge HPC systems such as ARCHER2 and Cirrus, along with other key platforms like the Edinburgh International Data Facility (EIDF).

Find out more about compulsory and optional courses

We link to the latest information available. Please note that this may be for a previous academic year and should be considered indicative.

• ​​You will gain knowledge and understanding in many topics relating to data science, including:
  • Machine learning
  • Data management
  • Data engineering
  • Statistics
  • Application of data science techniques
• ​You will demonstrate critical thinking about data science and how it can be innovatively applied to contemporary industrial and societal challenges.
• ​You will develop both written and oral presentation skills. You will demonstrate the ability to present and translate analytical findings and data interpretations for an interdisciplinary audience.
• ​You will engage confidently in interpersonal collaboration through intellectual curiosity and empathetic problem-solving.
• ​You will learn topics including but not limited to programming skills, skills for exploring and visualising data, and data analytics techniques, including training machine learning models.​

Roles within data science are in high demand, particularly with the increasing prevalence of generative AI.

This programme will prepare you for career opportunities related to data. Its multidisciplinary nature means that you can enter a broad range of sectors.

Professional links

​​The programme is coordinated by the Bayes Centre, the University’s innovation hub for Data Science and Artificial Intelligence. The Bayes Centre collaborates with companies in fields related to data research.

You will join the Bayes Community, which gives you access to industry contacts through online events and additional networking opportunities during your studies and after graduation.

These entry requirements are for the 2025/26 academic year and requirements for future academic years may differ. Entry requirements for the 2026/27 academic year will be published on 1 Oct 2025.

The programme is designed to be accessible. We welcome applicants who meet the standard academic entrance requirements and those with relevant work experience.

A UK 2:1 honours degree, or its international equivalent, in a numerate or computational discipline.

We will also consider a UK 2:2 honours degree, or its international equivalent, in Computer Science, Informatics, Software Engineering, Mathematics, Statistics, or similar.

We will also consider your application if you have relevant work experience (typically at least three years in a relevant field, working with data or programming). If you plan to apply on this basis, please include a detailed CV and outline how your professional background demonstrates your ability to undertake the programme in the Relevant Knowledge/Training section of your application. If you are unsure if you have relevant work experience, please email the Programme Director.

We strongly recommend that all applicants have SQA Higher or GCE A level Mathematics, or equivalent. We also recommend that students understand basic programming concepts and have some experience of computer programming (e.g. C, Fortran, Java, Python, R).

• Email the Programme Director

Students from China

This degree is Band C.

• Postgraduate entry requirements for students from China

International qualifications

Check whether your international qualifications meet our general entry requirements:

• Entry requirements by country

English language requirements

Regardless of your nationality or country of residence, you must demonstrate a level of English language competency which will enable you to succeed in your studies.

English language tests

We accept the following English language qualifications at the grades specified:

• IELTS Academic: total 6.5 with at least 6.0 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
• TOEFL-iBT (including Home Edition): total 92 with at least 20 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
• C1 Advanced (CAE) / C2 Proficiency (CPE): total 176 with at least 169 in each component.
• Trinity ISE: ISE II with distinctions in all four components.
• PTE Academic: total 65 with at least 59 in each component. We do not accept PTE Academic Online.
• Oxford ELLT: 7 overall with at least 6 in each component.

Unless you are a national of a majority English speaking country, your English language qualification must be no more than three and a half years old from the start of the month in which the programme you are applying to study begins. If you are using an IELTS, PTE Academic, TOEFL, Trinity ISE, or Oxford ELLT test, it must be no more than two years old on the first of the month in which the programme begins, regardless of your nationality. (Revised 14 January 2025 to include Oxford ELLT.)

Degrees taught and assessed in English

We also accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English speaking country, as defined by UK Visas and Immigration:

• UKVI list of majority English speaking countries

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English speaking countries (non-MESC).

• Approved universities in non-MESC

If you are not a national of a majority English speaking country, then your degree must be no more than five years old at the beginning of your programme of study.

Find out more about our language requirements:

• English language requirements

Additional programme costs

You will need to have access to a computer and the internet in order to participate in this programme.

Funding for postgraduate study is different to undergraduate study, and many students need to combine funding sources to pay for their studies.

Most students use a combination of the following funding to pay their tuition fees and living costs:

• borrowing money
  • taking out a loan
  • family support
• personal savings
• income from work
• employer sponsorship
• scholarships

Search for scholarships and funding opportunities:

• Search for funding