MSc in Data Science Technologies

With advances like generative AI transforming industries, there is an increasing demand for skilled data science specialists. The MSc in Data Science Technologies (DST) is designed for students with an established foundation in numerate sciences who are keen to elevate their proficiency in data systems and technologies.

DST

About the programme

This programme initiates a wide introduction into advanced data science topics, such as Machine Learning, data analytics, statistical analysis, programming, and data visualisation. It will equip you to handle complex data-driven challenges in industry and the public sector preparing you to further a career in a data-driven world. 

Key benefits

  • Access to Leading Academics and Industry Experts: Students benefit from direct access to the School of Informatics and EPCC, which operates the Advanced Computer Facility, presenting opportunities to engage with cutting-edge research and real-world data science applications. 

  • Multidisciplinary Learning Opportunities: Option to participate in courses across various schools within the University of Edinburgh, enabling a modern, truly multidisciplinary approach to data science. This exclusive feature enriches your educational experience, broadening your understanding and application of data science in multiple sectors. 

  • Industry-Linked Curriculum: The course has strong links with industry, providing you with exposure to real-world data science challenges and technologies through projects and the dissertation. 
  • Career Prospects: Graduates are well-prepared to further or advance careers in data science. The skills developed during the programme are in high demand across various sectors, including finance, healthcare, technology, and consultancy.  
  • Programme of Events: A diverse schedule of academic and social events, such as alumni career talks and specialised research discussions, enriching your learning experience and professional network. 

  • Comprehensive Student Support: Benefit from a tight-knit community with access to expert academic support throughout your studies and supportive career services—all designed to foster both your academic and personal growth. 

  • Graduate in Edinburgh: Complete your MSc with an in-person graduation ceremony at the historic University of Edinburgh campus. 

Programme structure

Compulsory modules: 

  • Practical Introduction to Data Science: Providing you with foundational skills in data handling and analysis. 
  • Intro to Probability and Statistics: Equipping you with statistical tools, covering fundamentals to advanced concepts that are critical for data science. 
  • Applied Machine Learning: Focusing on the technical and theoretical basis of machine learning models and their application in real-world settings. 
  • Programming Skills: Teaches robust programming concepts which are applicable across various languages, emphasising reproducibility and testing. 

Non-compulsory modules  

Customise your learning by selecting elective modules that align with your interests in technical areas of data science, including machine learning and high-performance computing. 

Dissertation 

A robust research project with academics from the School of Informatics or EPCC. Offering you the opportunity to delve deeply into a chosen research topic with substantive academic supervision. 

Entry requirements

 

Academic Requirements: A UK 2:1 honours degree (or international equivalent) in a numerate or computational discipline is typical. A UK 2:2 degree in Computer Science, Informatics, Software Engineering, Mathematics, Statistics, or a similar field may also be considered. 

Work Experience: Applicants with relevant professional experience (typically at least three years) involving data or programming, who do not meet the academic requirements, are also encouraged to apply. 

Recommended Skills: Familiarity with SQA Higher or GCE A level Mathematics or equivalent is strongly recommended. We also advise some experience in computer programming (like C, Fortran, Java, Python, or R).  

 

 

 

Study options and online learning 

1-year full-time or 3-year part-time, structured to accommodate professionals balancing career commitments. 

This programme offers the opportunity to deepen your expertise through close collaboration with academics on various projects, with a comprehensive support structure that enhances both your study skills and professional development. 

You will be taught by renowned experts in their field using innovative synchronous and asynchronous teaching methods to foster a collaborative learning community. 

 

Who should apply 

Ideal candidates are those who enjoy tackling challenging technical problems, developing new analytical methods, and are interested in technological innovations in data science. 

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