MSc Data Science

1 Year On Campus Masters Program

University of Edinburgh

Program Overview

The MSc Data Science at the University of Edinburgh equips students with key skills in statistical programming, machine learning, and large-scale data analysis, ideal for those from quantitative backgrounds pursuing data-driven careers. The curriculum balances technical coursework with practical projects for roles in tech, analytics, or research.

Curriculum Structure

Semester 1:
Modules include "Statistical Programming," "Machine Learning and Pattern Recognition," and "Data Analytics with R," covering core data analysis and modeling techniques.

Semester 2:
Key units: "Text and Data Mining," "Large Scale Data Analysis," and "Database Theory and Applications," focusing on handling big data and real-world datasets.

Dissertation:
Students complete a supervised research project solving a current data science problem.

Focus areas

"Machine learning, data mining, pattern recognition, statistical modelling"

Learning outcomes

"Programming, data analysis, problem-solving, communication"

Professional alignment (accreditation)

Accredited by the British Computer Society (BCS).

Reputation (employability rankings)

QS Top 30 globally for Computer Science; high graduate employability.

Experiential Learning (Research, Projects, Internships etc.)

Students gain in-depth practical skills on the MSc Data Science at the University of Edinburgh through supervised projects, group assignments, and access to dedicated facilities within the School of Informatics’ Appleton Tower. All coursework emphasizes active engagement with professional data science tools, advanced hardware labs, and support from specialized advisers, allowing students to apply theory directly to real-world challenges. The transition from classroom learning to applied experience is seamless, ensuring graduates acquire both competence and confidence with industry practices:

- Dedicated Appleton Tower labs equipped for advanced programming, statistical analysis, and machine learning.
- Access to High Performance Computing (HPC) platforms including ARCHER2, Cirrus, and the Edinburgh International Data Facility (EIDF).
- Group projects and supervised independent research as part of the master's project/dissertation.
- Industry-standard software and digital platforms used for analysis, modeling, and data visualization (Python, R, SQL, etc.).
- Comprehensive library resources, specialist journals, and databases supporting research and study.

Progression & Future Opportunities

Graduates from this MSc are highly sought after for their strong problem-solving, technical, and research abilities. They move into both academic and industry roles, contributing to cutting-edge projects in technology, data, and research. Typical career paths include: software engineer, data scientist, machine learning specialist, research associate.

Students benefit from Edinburgh’s strong employer links and dedicated career services:

Careers Service support with tailored workshops, employer presentations, and one-to-one guidance.
Industry partnerships with leading tech firms such as Amazon, Google, Skyscanner, and Microsoft, offering recruitment pipelines and project collaborations.
Graduate outcomes: Edinburgh consistently reports high employment and further study rates for informatics graduates.
Salary prospects: Computer Science graduates from Edinburgh command competitive UK and international salaries.
Long-term value: Accreditation and international recognition of the University of Edinburgh enhances professional credibility worldwide.

Further Academic Progression:
Graduates may continue with doctoral studies (PhD) in areas such as Artificial Intelligence, Software Engineering, or Theoretical Computer Science at Edinburgh or other top global universities.

Additional Information & Requirements

A UK 2:1 honours degree, (60-69%), in informatics, artificial intelligence, cognitive science, computer science, electrical engineering, linguistics, mathematics, philosophy, physics or psychology.
Competence in programming is essential. During your degree you must have completed a programming course in at least one of the following: C/C++, Java, Python, R, Matlab, Haskell, ML.
USA- You should have an undergraduate degree from a recognised university or college with a minimum 3.25 GPA.
India -The minimum score you should have achieved is 60% or more / GPA of 8 on a 10-point scale in your Bachelor's study. If you are studying at university listed on the UoE Indian Universities List you should achieve 55% or more / GPA of 7.0 on a 10-point scale.
IELTS Academic: total 7.0 with at least 6.5 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
TOEFL-iBT (including Home Edition): total 100 with at least 23 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.