Data Science Technologies (Online Learning) MSc

Level: Postgraduate taught

Subject: Data Science

Year of entry: 2026

Study options

Mode of study

Online learning

Full-time
  • MSc | 1 year | Start date: September 2026
Part-time
  • MSc | 3 years | Start date: September 2026

Key facts

Accreditation

Not available

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About the degree programme

With advances like generative AI transforming industries, there is an increasing demand for skilled data specialists. Being proficient in data science is growing more important across fields as diverse as the sciences, medicine, arts, and humanities.

This online MSc is designed to provide you with the essential skills needed to begin or further a career in a data-driven world. It combines elements from fields such as statistical analysis, machine learning, programming and data visualisation to help inform strategic decision making, optimise processes and solve complex, real-world problems.

We go beyond teaching data science; we lead innovations in the field. Our programme will connect you with a high-profile network of research centres, institutes, and innovation centres across the University, empowering you to comprehensively explore the multidisciplinary nature of data science. 

You will gain a strong foundational knowledge of the technological advances most relevant today, preparing you for practical, impactful applications in diverse real-world scenarios.

Study options

You can study this programme over 1 year full-time or 3 years part-time.  

If you study full-time, you will undertake your dissertation and project preparation course with the School of Informatics. 

If you study part-time, you can choose to undertake your dissertation and the associated project preparation course with the School of Informatics or EPCC (formerly the Edinburgh Parallel Computing Centre). You will also have more option courses to choose from. 

You can choose to exit with a Postgraduate Diploma (PgDip) or Postgraduate Certificate (PgCert), if you complete 120 or 60 credits respectively.

Programme benefits

  • Gain hands-on experience with various real-world data science applications.
  • Get the essential knowledge and research training needed for aspiring data science practitioners and researchers.
  • Understand the applications and impacts of data-intensive technologies, including generative AI.
  • Develop critical and creative thinking skills to navigate the ethical, governance, and social dimensions associated with data science technologies.
  • Enjoy the flexibility of our online courses and balance your studies with personal and professional commitments.

Tuition fees

Tuition fees by award and duration

Tuition fees for full-time and part-time options are listed for one academic year.

Full-time
Part-time

Graduate discount

If you are a University of Edinburgh graduate, you will be eligible for a 10% discount on your tuition fees for this programme. You may also be eligible if you were a visiting undergraduate student.

Find out how to receive your graduate discount

Deposit

You do not have to pay a deposit to secure your place on this programme.

Funding opportunities

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

Explore sources of funding for postgraduate study

Search for other funding opportunities

You can find scholarships, bursaries and other funding you might be eligible for on our Scholarships and Student Funding site.

Postgraduate funding opportunities

These entry requirements are for the 2026-27 academic year and requirements for future academic years may differ. Entry requirements for the 2027-28 academic year will be published on 1 Oct 2026.

Qualifications

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

International qualifications

To find international equivalent qualifications, select where you studied from the country or region list.

English language requirements

You must prove that your English language abilities are at a high enough level to study this degree programme.

This is the case for all applicants, including UK nationals.

You can meet our English language requirements with one of the following:

  • an English language test
  • a degree that was taught and assessed in English
  • certain professional qualifications

English language tests we accept

We accept any of the following English language tests, at the specified grade or higher:

  • 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.
  • Oxford ELLT: total 7 with at least 6 in each component.
  • Oxford Test of English Advanced: total 145 with at least 135 in each component.
How old your English language tests can be
Tests no more than two years old

The following English language tests must be no more than two years old on the 1st of the month in which your programme starts, regardless of your nationality:

  • IELTS Academic
  • TOEFL-iBT (including Home Edition)
  • Trinity ISE
  • Oxford ELLT
  • Oxford Test of English Advanced
Tests no more than three and a half years old

All other English language tests must be no more than three and a half years old on the 1st of the month in which your programme starts, regardless of your nationality.   

Degrees taught and assessed in English

We 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

How old your degree can be

If you are not a national of a majority English-speaking country, then your degree must be no more than five years old on the 1st of the month in which your programme starts.

This time limit does not apply to your degree if you are a national of a majority English-speaking country.

Find out more about our English language requirements

Find out about other English language qualifications we accept, including professional qualifications.

English language requirements

What you will study

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

Find courses for this programme

Find out what courses you can study on this programme and how each of them are taught and assessed.

The courses on offer may change from year to year, but the course information will give you an idea of what to expect on this programme.

Full-time
Part-time

We link to the latest information available. This may be for a previous academic year and should be considered indicative.

Disclaimer

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.

Teaching and assessment

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.

Learning outcomes

  • ​​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.​

Support for your studies

You will have access to a range of support services if you need them throughout your degree.

We will assign you to a student adviser, and this should be the first person to contact if you need help. They can guide you to other University service teams depending on what support you need.  

How we support you

Our academic staff

Where you will study

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).

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.

Watch our video to see how online learning works.

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.  

See 'Find courses' in Programme details for more information about study time commitment

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 and computing help

Opportunities to attend in person

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

Support for online students

Career opportunities

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. 

Further study

After completing this programme, you may wish to consider applying for a PhD or other research programme.

Applying for research degrees

Moving on to a PhD (advice from the University's Careers Service)

Careers Service

Our Careers Service can help you to fully develop your potential and achieve your future goals. 

The Careers Service supports you not only while you are studying at the University, but also for up to two years after you finish your studies. 

With the Careers Service, you can: 

  • access digital resources to help you understand your skills and strengths
  • try different types of experiences and reflect on how and what you develop
  • get help finding work, including part-time jobs, vacation work, internships and graduate jobs
  • attend careers events and practice interviews
  • get information and advice to help you make informed decisions 

Visit the Careers Service website 

How to apply

You apply online for this programme. After you read the application guidance, select your preferred programme, then choose 'Start your application' to begin.

If you are considering applying to more than one programme, you should be aware that we cannot consider more than 3 applications from the same applicant.

When to apply

Programme start date Application deadline
14 September 2026 31 August 2026

We encourage you to apply at least one month prior to entry so that we have enough time to process your application. If you are also applying for funding or will require a visa then we strongly recommend you apply as early as possible.

When to submit your supporting documents

You must submit all supporting documents by the application deadline, or we will be unable to consider your application. Regardless of when you apply, you have 28 days from submitting your application to supply any supporting documents through the Application Hub, after which we will automatically reject your application.

Application fee

There is no fee to apply to this programme.

What you need to apply

As part of your online application, you will need to provide: 

You will also need to submit some or all of the following supporting documents:

When you start your application, you will be able to see the full list of documents you need to provide.

Apply

Select the award, duration and delivery mode you want to study. Then select the start date you want to apply for.

After you apply

Once you have applied for this programme, you will be able to track the progress of your application and accept or decline any offers.

Checking the status of your application

We will notify you by email once we have made a decision. Due to the large number of applications we receive, it might take a while until you hear from us.

Receiving our decision

What to do if you receive an offer:

General enquiries

Contact our enquiry team about eligibility, how to apply, and the admissions process.

Contact us

Academic contact

Contact the Programme Director, Dr Adam Carter, about academic enquiries about the programme or your suitability.

Dr Adam Carter

Programme Director

Contact details

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