Overview
Data science is revolutionising every area of science, engineering and commerce. It offers the potential for huge societal and economic benefits.
Data science extracts insights and knowledge from large and complex datasets. It uses a wide range of techniques and methods to do this.
We created the Data Science MSc with several high-profile industry leaders. It aims to address the skills shortage in data analytics.
Our Master’s in Data Science brings together students and industry practitioners to develop and translate new technologies into industry practice.
You’ll receive a comprehensive grounding in the theory and application of data science. You’ll develop a multi-disciplinary combination of skills in statistics and computer science. You’ll also be able to apply these skills to real problems in a given application area.
You’ll learn to analyse data and uncover patterns, trends and correlations. Topics covered also include:
- data visualisation
- statistics
- machine learning
- data engineering
You’ll benefit from our substantial expertise in data science. We focus on a wide range of application areas, including:
- healthcare
- transport
- cybersecurity
- smart cities
- manufacturing
This data science course is part of the following suite:
- Data Science and AI MSc
- Data Science with Visualization MSc
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Important information
We’ve highlighted important information about your course. Please take note of any deadlines.
Your course and study experience – disclaimers and terms and conditions
Please rest assured we make all reasonable efforts to provide you with the programmes, services and facilities described. However, it may be necessary to make changes due to significant disruption, for example in response to Covid-19.
View our Academic experience page, which gives information about your Newcastle University study experience for the academic year 2024-25.
See our terms and conditions and student complaints information, which gives details of circumstances that may lead to changes to programmes, modules or University services.
Qualifications explained
Find out about the different qualification options for this course.
MSc – Master of Science
An MSc is a taught Master’s degree. It usually involves the study of a science-related subject.
You’ll usually study an MSc full-time over 12 months.
A Master of Science is typically awarded for the successful completion of 100 credits of taught modules and an 80-credit dissertation or research project.
Find out about different types of postgraduate qualifications.
What you’ll learn
This Data Science MSc has three phases.
Phase one
We’ll introduce you to the core knowledge and skills in statistics and computer science.
These modules are taught as an intensive block, meaning you’ll be taught two modules simultaneously.
Phase two
Phase two consists of more advanced technical modules, as well as a group project.
We’ll introduce the aspects that govern all areas of data science practice, including:
- professionalism
- legislation
- ethics
During the group project, you’ll develop and evaluate a data science solution to a complex, real-world problem. You’ll work in an industry organisation. They can be a regional, national or charitable organisation. You’ll propose a data science project in that company, institute, or area of research.
Phase three
In your final phase, you’ll work on an individual research project. It’ll give you an opportunity to:
- develop your knowledge and skills
- work in a research or development team
You can develop your project:
- at the University under an academic supervisor
- by securing an industrial placement
- working with your current employer
You’ll have one-to-one supervision from an experienced staff member. If needed, you’ll also get supervision from industry partners.
Modules
You will study modules on this course. A module is a unit of a course with its own approved aims and outcomes and assessment methods.
Course content changes
Module information is intended to provide an example of what you will study.
Our teaching is informed by research. Course content changes periodically to reflect developments in the discipline, the requirements of external bodies and partners, and student feedback.
Full details of the modules on offer will be published through the Programme Regulations and Specifications ahead of each academic year. This usually happens in May.
To find out more please see our terms and conditions.
Optional modules availability
Some courses have optional modules. Student demand for optional modules may affect availability.
Further compulsory module information
If you have permission from the Degree Programme Director, you can swap Advanced AI (10 credits) with Bayesian Methodology (10 credits).
Optional module information
You take either Complex Data Visualization (10 credits) or Deep Learning (10 credits).
How you’ll learn
The School of Computing and School of Mathematics, Statistics and Physics deliver this course.
You’ll be taught using a range of methods, including:
- seminars
- lectures
- practical classes
- group and individual project work
- guided independent reading
- self-directed learning
Depending on your modules, you’ll be assessed through a combination of:
- Dissertation
- Oral presentation
- Poster
- Report
- Written examination
Additional assessment information
Our MSc in Data Science uses both formative and summative assessments.
These assessments will:
- evaluate your overall understanding of the course content
- identify your strengths and areas for improvement
- encourage continuous learning and personal development
Formative assessments
Formative assessments are designed to provide ongoing feedback and support throughout the course. These assessments will help you identify your strengths and areas for improvement, fostering continuous development.
Examples of formative assessments include:
- Weekly quizzes: Short quizzes at the end of each module to test your understanding of the material.
- Assignments: Regular assignments that involve practical data analysis tasks and problem-solving exercises.
- Class participation: Active participation in seminars, workshops, and group discussions to enhance learning through interaction.
- Peer reviews: Opportunities to review and provide feedback on classmates’ work, promoting collaborative learning.
Summative assessments
Summative assessments occur at the end of each module. They’re designed to evaluate your overall comprehension and mastery of the course content.
These assessments will contribute to your final grade.
Examples of summative assessments include:
- Coursework: Projects where you apply the skills and knowledge gained throughout the course to a real-world data science problem.
- Presentations: Oral presentations of your projects and research findings to assess your communication skills and ability to articulate complex ideas clearly.
There’ll be no written exams during your Data Science MSc.
The School of Computing has a dedicated Wellbeing Advisor who understands the needs of our students.
They can be a confidential listening ear and provide guidance on a range of wellbeing issues.
Your teaching and learning is also supported by Canvas. Canvas is a Virtual Learning Environment. You’ll use Canvas to submit your assignments and access your:
- module handbooks
- course materials
- groups
- course announcements and notifications
- written feedback
Throughout your studies, you’ll have access to support from:
- peers
- academics
- personal tutors
- our University Student Services Team
- student representatives
You’ll also be assigned an academic member of staff. They will be your personal tutor throughout your time with us. They can help with academic and personal issues.
The staff delivering this course are internationally recognised for their contributions to data science. Many of them have extensive experience working in industry and academia.
Your development
Improve your research skills through an extended research project. The project can include:
- a literature review
- problem specifications
- design
- implementation
- analysis
You’ll also develop your practical skills, including solving computational problems at scale and demonstrating appropriate scalable computational workflows and solutions applied to large information-handling problems.
After completing the course, you’ll be able to:
- design and implement new software packages
- apply computing, mathematical and statistical techniques to data storage and analysis
- confidently use the latest programming languages and software tools
- build and analyse predictive models from data
Your future
Careers
The course prepares you for a wide range of careers, including roles as:
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- Business Intelligence Analyst
- Research Data Scientist
- Quantitative Analyst
- AI Specialist
Graduates from this course have undertaken various roles including:
- Senior Software Engineer at Sage
- Data Analyst at Vodafone
- Data Engineer at Virgin Atlantic
- Data Scientist at Stanley Black & Decker, Inc.
We have strong industry and business links with the following companies:
- Sage
- National Innovation Centre for Data (NICD)
- Nissan
- Northumbrian Water
These connections provide you with numerous benefits, including internship opportunities, guest lectures, industry-sponsored projects, and potential employment upon graduation.
Further study
This course provides a route into PhD level study, offering a robust foundation in both theoretical and applied aspects of Data Science. As a graduate, you’ll be prepared to pursue advanced research opportunities and contribute to the academic community through doctoral programs.
Data science careers support
Our dedicated careers support team offers specialised guidance tailored to Data Science students. This includes:
- career planning
- workshops on resume-building and interview techniques
- networking events with industry leaders
- job fairs focused on data science and analytics
- access to an extensive alumni network for mentorship and job referrals
- support for start-ups
Our Careers Service
Our award-winning Careers Service is one of the largest and best in the country, and we have strong links with employers. We provide an extensive range of opportunities to all students through our ncl+ initiative.
Quality and ranking
- 42% of our research is classified as 4* world-leading research – Research Excellence Framework 2021
- 65% increase in research power since 2014 – Research Excellence Framework 2021
- Global Top 130 University – QS World University Rankings 2025
- Global Top 170 University – Times Higher Education World University Rankings 2024
- Top 25 in the UK and Top 100 in the world for sustainable development – Times Higher Education Impact Rankings 2024
Professional accreditation and recognition
British Computer Society (BCS)
The course is accredited by the British Computer Society (BCS). The BCS is the chartered institute for IT. Studying a BCS-accredited degree provides the foundation for professional membership of the BCS on graduation and is the first step to becoming a chartered IT professional.
All professional accreditations are reviewed regularly by their professional body
Recognition of professional qualifications outside of the UK
From 1 January 2021 there is an update to the way professional qualifications are recognised by countries outside of the UK
Facilities
National Innovation Centre for Data
The Newcastle Helix campus is home to the UK’s National Innovation Centre for Data (NICD). NICD runs projects with organisations to help them acquire new skills and innovate through data.
Urban Sciences Building
The School of Computing is based in the £58 million Urban Sciences Building (USB), a flagship development located on the £350 million Newcastle Helix regeneration site in the heart of Newcastle. It brings together:
- academia
- the public sector
- communities
- business and industry
Postgraduate student facilities
As a Master’s student, you’ll have access to specialist teaching spaces and facilities in the USB. These are only available to postgraduate students.
Wellbeing and inclusivity are at the heart of our School. The USB has several wellbeing spaces for students, including:
- The Retreat: A sensory space with relaxing stimuli to distract from busy student life.
- Wellbeing room: Designed for relaxation and quiet time. Here you can take a moment to breathe and unwind. It can also be used by students with special medical requirements.
- Prayer room: For all faiths and none, this space can be used for prayer or quiet reflection.
Entry requirements
The entrance requirements below apply to 2025 entry.
Academic entry requirements
A 2:1 BSc honours degree, or international equivalent, in:
- computer science
- mathematics
- statistics
- an engineering discipline with programming experience
We have a strong track record of admitting applicants from a non-standard background and individuals with strong relevant work experience are encouraged to apply and will be considered on an individual basis.
