Data Science MSc
2025-26 Year of entry
Join an award-winning course and develop the skills to be a data scientist, a role that is becoming essential across the full range of industries
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Why choose Data Science MSc at Middlesex?
This master's course has been designed to offer those with a familiarity in mathematical science or computing an opportunity to develop a set of skills for future employment in a way that builds on your existing knowledge and skills. After finishing the course, you'll be ready to enter a career as a data scientist.
You'll focus on the interconnected areas of machine learning, visual analytics and data governance, and learn to strike a balance between theory, practice, and the acquisition of industrially-relevant languages and packages.
You'll also be exposed to cutting-edge contemporary research activity within data science that will equip you with the potential to pursue a research-based career, and, in particular, further PhD study at Middlesex.
What you will gain
Some of the benefits of joining us on this course include:
- A chance to explore theoretical and practical aspects of the subject while gaining industry-recognised skills
- Studying a unique fusion of machine learning, visual analytics and corporate data governance
- Opportunities to apply machine learning and visual analytics to any data source
- Learning industry-relevant languages, packages and platforms such as Python, scikit-learn, Amazon Web Services (AWS), Apache Hadoop and Apache Spark.
Placements
Industrial placement is offered as an optional opportunity for full-time students studying at the London campus.
3 great reasons to pick this course
About your course
Your studies will focus on the intertwining areas of machine learning, visual analytics and data governance. You'll explore the theoretical underpinnings of the subject while gaining practical hands-on experience. You'll build on your existing knowledge and skill set to gain essential knowledge that will be readily applicable to a career in data science.
This module aims to provide students with state-of-the-art AI techniques in the machine vision field so that students are able to work in the areas of autonomous, data visualisation and video/image analysis. Deep learning was incepted from image processing due to its large information an image carries beyond human’s capacity. This module introduces the fundamentals in image representation, image processing and image applications. In addition, the advances of computer vision to simulate human vision based on deep leaning, e.g. driverless cars and satellite navigation systems, will be investigated. Existing deep learning architectures, e.g., Alexnet, ResNet, GAN will be studied in relation to feature extraction, segmentation, and classification. Comparison with current machine learning algorithms, e.g. SVM, K-means will be conducted. The programming languages will apply Matlab, Python, and Jupyter Notebook.
This module aims to provide students with a solid foundation in the concepts, techniques, and algorithms of machine learning and artificial intelligence, from Naïve Bayes basics to Transformer Models (as utilised in LLMs), combining theoretical principles with practical applications. Students will explore core paradigms such as supervised & unsupervised learning in both sequential and non-sequential contexts, gaining an understanding of key mathematical and statistical foundations, The module covers learning from both labelled and non-labelled exemplar data, along with appropriate training protocols. Students will hence work with regression, classification, clustering, ensemble learning, deep/non-deep neural networks, while learning to manage issues of bias, variance and structural risk in order to arrive at bespoke training strategies. By critically evaluating these models using performance metrics and applying techniques to optimise and generalise them, students will develop the skills to design, implement, and knowledgably deploy machine learning & AI solutions for real-world data-driven problems.
Big data needs secure data pipelines moving data from a source, usually the Cloud, to target systems while being transformed by a range of often complex interdependent processes.
A well-designed big data pipeline architecture needs to adhere to use case requirements while being efficient, secure and maintainable.
The challenges we address in this module are to understand and learn to apply techniques that enable to solve problems related to the complex transformation of data as part of the distributed, complex processing of big data. We will study and apply in practical case studies how big data is organized in the Cloud, how data pipelining techniques enable to query distributed data bases and how to secure such pipeline architectures.
This module aims to develop students' ability to think critically and adaptively in applying data science techniques to complex, real-world challenges across both industry and research. Students will gain experience navigating the full data science lifecycle, from data collection and preprocessing to model development, evaluation, and deployment. Through hands-on engagement with analytical tools, machine learning, and natural language processing, they will cultivate problem-solving skills and a deep understanding of how data-driven insights drive innovation in both professional and academic settings.
This module fosters a comprehensive understanding of the methods, theories and techniques relevant to interactive visual data analysis. Students will learn relevant principles and practices in visual data analysis design, implementation, and evaluation. They will gain experience in researching, designing, implementing, and evaluating their own visual analysis solutions, using both off-the-shelf toolkits and data visualisation programming libraries. The module offers students’ knowledge to support future employment or research in the fast-developing areas of data science, particularly visual analytics.
This module will give students a critical understanding of legal and ethical issues regarding the use and management of data in the context of data science. Students will gain knowledge of how data should be processed in the context of legal frameworks focusing on issues such as intellectual property rights, privacy, data protection and contracting among others. Students will also explore legal and ethical challenges related to machine learning and the governance of trustworthy and responsible artificial intelligence applications. Further students will be equipped with the necessary foundations to develop high professional standards when working as data scientists.
This module aims to equip students with the tools and techniques necessary to design and implement effective and efficient experiments in the context of computer science projects. It will also provide students with the skills required for statistical analysis, hypothesis testing, and presenting results within the scope of computational research and data-driven studies.
The primary aim of this module is to develop student’s employability skills and support their search for a placement. The module will include communication, team working, negotiation and problem-solving skills development as well as practical workshops on selection methods, CV's, cover letters, interview preparation and techniques. The module will also introduce students to other methods that will include aptitude test and assessment centres.
The objective of this module is to offer students a three-month (12-week) work placement that is directly applicable to their studies, encouraging them to engage in critical reflection on their learning through hands-on experience. Additionally, it seeks to develop students' intellectual and interpersonal abilities, thereby deepening their critical comprehension of real-world applications.
The objective of this module is to offer students an extended (minimum 36 weeks) work placement that is directly applicable to their studies, encouraging them to engage in critical reflection on their learning through hands-on experience. Additionally, it seeks to develop students' intellectual and interpersonal abilities, thereby deepening their critical comprehension of real-world applications.
To find out more about this course, please download the Data Science MSc programme specification (PDF).
Register your interest
Sign up now to receive more information about studying at Middlesex University London.
Teaching
You'll be taught by an experienced teaching team with a wide range of expertise and professional experience.
How we'll teach you
You'll develop your knowledge and understanding of the subject through a combination of traditional lectures, small group discussions, small group and individual exercises, lab sessions and an individual research project.
Throughout your studies, we'll encourage you to undertake independent study both to supplement and consolidate what you're learning and to broaden your individual knowledge and understanding of the subject. Critical evaluation and selection of techniques and solutions will help you relate theory to practice.
You'll be taught by an experienced teaching team with a wide range of expertise and professional experience.
You will be based at our north London campus.
Whether you are studying full or part-time – your course timetable will balance your study commitments on campus with time for work, life commitments and independent study.
If you're studying full-time, you'll typically be expected to attend four modules per week, with each module consisting of three hours of weekly class time. If you're studying part-time, you'll generally be expected to attend two modules per week.
We aim to make timetables available to students at least 2 weeks before the start of term. Some weeks are different due to how we schedule classes and arrange on-campus sessions.
In a typical year, you’ll spend about 1200 hours on your course.
Outside of teaching hours, you’ll learn independently through reading articles and books, working on projects, undertaking research, and preparing for assessments including coursework and presentations.
Typical weekly breakdown
A typical week looks like this:
|
Learning |
Hours per week |
|
On-campus Part-Time |
12 |
|
On-Campus Full-Time |
6 |
|
Online Part-Time |
12 |
|
Online Full-Time |
6 |
|
Independent study Part-Time |
14 |
|
Independent study Full-Time |
28 |
For placement, (work-based learning or year abroad, as appropriate), Full-Time is 3 months (15 months programme) or minimum of 36 weeks (24 months programme), and for Part-Time this is N/A.
Learning terms
On-campus: This includes tutor-led sessions such as seminars, lab sessions and demonstrations as well as student-led sessions for work in small groups.
Online learning: This is teaching that is delivered online using tools like Skype or Zoom, as well as work that you do yourself using online teaching resources.
Independent study: This is the work you do in your own time including reading and research.
Part-time study
You can also study this course part-time.
We have a strong support network online and on campus to help you develop your academic skills. We offer one-to-one and group sessions to develop your learning skills together with academic support from our library, IT teams and learning experts.
Throughout the year, we'll assess your technical skills through a series of assignments. Every week, we'll give you lab tasks designed to match the content covered in the lecture. We expect these tasks to be completed during the lab and you'll receive timely feedback assessment.
We'll assess your understanding through a variety of assessment methods including coursework projects, in-class activities, and a portfolio of data science tasks.
Feedback
You'll evaluate your work, skills and knowledge and identify areas for improvement. Sometimes you'll work in groups and assess each other's progress.
Each term, you'll get regular feedback on your learning.
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North London campus
Our north London campus is just 20 minutes from central London, giving you easy access to everything this vibrant city has to offer. Make the most of incredible professional opportunities, cultural experiences, and more affordable living, all within a supportive and close-knit campus community.
Learn moreFacilities and support
Our Sheppard Library has over 1000 study areas and 600 computer spaces
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Make the most of our free laptop loans, e-books, printing and photocopying
Learn more/101x0:851x634/prod01/channel_3/media/middlesex-university/courses-images/ug-courses/facilities/Sheppard-Library-Facilities-2014-40.jpg)
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We have one of London's biggest and best university campuses with everything you need in one place.
Our facilitiesFacilities
The Department of Computer Science teaching and learning approach aligns with the University's goal to promote learner autonomy and resource-based learning. To enhance the experience of Computer Science students:
Specialist Laboratories and Software
Students have access to state-of-the-art labs equipped with industry-standard software and hardware. These facilities support areas such as data analysis, machine learning, and data visualisation. Labs are available for both structured teaching sessions and self-directed projects.
Induction and Diagnostic Assessments
All new Computer Science students participate in an induction programme, which may include early diagnostic testing in numeracy, programming logic, and technical literacy. The University offers one-to-one tutorials and workshops for students needing additional support.
Digital and Networked Facilities
Students are provided with a personal email account, secure networked storage, and remote access to essential software and systems, enabling effective study and collaboration.
Programme and Module Handbooks
An electronic version of the programme handbook is posted on My Learning. distributed during enrolment. In addition, Module-specific handbooks and online learning resources covering foundational and advanced computer science topics are also provided.
Library and Support Services
Extensive library resources, including access to technical books, academic journals, and digital archives, are available to support Computer Science learning. Students can also access personalised advice and guidance on academic and personal matters through the student support services.
Group Tutorials and Continuous Feedback
Group tutorials are provided for each module, enabling interactive learning and in-depth discussions on all taught modules. Feedback is consistently provided on all formative assessments to facilitate continuous improvement.
Research and Collaboration Opportunities
The department's research initiatives in fields such as artificial intelligence, machine learning, computational data science, and data visualisation inform teaching. Students may have the chance to collaborate on research projects with faculty members, gaining hands-on experience in cutting-edge developments.
Student support
We offer lots of support to help you while you're studying including financial advice, wellbeing, mental health, and disability support.
Additional needs
We'll support you if you have additional needs such as sensory impairment or dyslexia. And if you want to find out whether Middlesex is the right place for you before you apply, get in touch with our Disability and Dyslexia team.
Wellness
Our specialist teams will support your mental health. We have free individual counselling sessions, workshops, support groups and useful guides.
Work while you study
Our Middlesex Unitemps branch will help you find work that fits around uni and your other commitments. We have hundreds of student jobs on campus that pay the London Living Wage and above. Visit the Middlesex Unitemps page.
Careers
How can the Data Science MSc support your career?
All industries now use data and data science and data analytics are increasingly identified as essential industrial activities. The position of data scientist is rapidly becoming a required post for any company that wishes to take full advantage of the data that they collect. This course is designed to give you the skills to step into a career as a data scientist in a wide range of industries and companies.
Our university's postgraduate courses have been recognised for their ability to support your career.
We are a top 10 UK University for employability (UniCompare Rankings 2025), and a top 10 UK university for industry connections and funding in Times Higher Education Young University Rankings 2024.
As a result, graduates of the MSc Data Science programme will be positioned to enter a wide range of career paths, equipped with both the deep technical expertise and the practical, research-informed approach necessary to excel.
Specifically, graduates may find opportunities in sectors such as:
- Technology and Software Development. Designing and implementing machine learning algorithms, developing AI-powered applications, and creating data-driven solutions in industries such as fintech, e-commerce, and gaming.
- Healthcare and Biotechnology. Applying data science techniques to medical research, personalised medicine, bioinformatics, and health data analysis to improve patient outcomes and advance medical discoveries.
- Finance and Consulting. Using advanced analytics to analyse financial markets, assess risks, create predictive models, and provide data-driven consulting services in areas like investment banking, insurance, and fintech.
- Government and Public Policy. Leveraging data science to inform public policy decisions, optimise resource allocation, and analyse social, economic, and environmental data for governmental planning and strategic initiatives.
- Retail and Marketing. Applying data analytics to understand consumer behaviour, optimise marketing strategies, and develop recommendation systems that drive customer engagement and business growth.
- Research and Academia. Pursuing further academic study, such as PhD programmes in data science, machine learning, or AI, and contributing to cutting-edge research in social computing, artificial intelligence, and big data analytics.
MDXWorks
MDXworks, our employability service, will help you make the most of your experience and connections to achieve your career goals. You'll have unlimited access to one-to-one advice and support from specialists in your sector plus 24/7 online support. You can also make the most of events and networking opportunities, on and off campus.
Start-Up accelerator programme
We can help you bring your business idea to life with our MDXcelerator programme which includes masterclasses with high-profile entrepreneurs, workshops, mentoring opportunities and competitions to secure seed funding.
Global alumni network
You’ll be studying with students from 122 countries who’ll become part of your personal global network. You'll learn how to work with a global mindset and make invaluable connections on your course for your chosen industry.
Entry requirements
Qualifications
We welcome your application regardless of your background or experience.
For this course, ideally, we are looking for these qualifications:
- A minimum 2:2 honours degree in computer science, or a minimum 2:2 in a relevant subject (such as maths, physics, or engineering), or two or more years of relevant working experience (such as programming, data analytics or machine learning)
- Graduate-level professional qualifications.
If you have relevant qualifications or work experience, we may be able to count this towards your entry requirements.
We welcome students from the UK and all over the world. Join students from over 122 countries and discover why so many international students call our campus home:
- Quality teaching with top facilities plus flexible online learning
- Welcoming north London campus that's only 30 minutes from central London
- Work placements and networking with top London employers
- Award-winning career support to get you where you want to go after university.
Qualifications
We accept a wide range of international qualifications. Find out more about the accepted qualifications on your country's support page. If you are unsure of the suitability of your qualifications or would like help with your application, please contact your nearest international office.
English language
You will need to meet our English language requirements. And, don’t worry If you don't meet our minimum English language requirements, as we offer a Pre-sessional English course.
Visas
To study with us in the UK, you might need a Student visa. Please check to see if this applies to you.
Apply as early as possible to make sure you get a place. You can submit your application before you receive your final qualification.
Personal statements
Make sure that you highlight your best qualities in your personal statement that are relevant to this course. Such as forward-thinking, creative and collaborative.
Interviews
You won’t be required to attend an interview.
Find out more about how to apply for postgraduate taught courses and watch our step-by-step video.
Fees and funding
The fees below refer to the 2025/26 academic year unless otherwise stated.
UK students1
Full-time students: £11,300
Part-time students: £75 per credit
Part-time students: £37 per dissertation credit
International students2
Full-time students: £18,000
Part-time students: £120 per credit
Part-time students: £60 per dissertation credit
Placement
£3,000 per year
Additional costs
We cover your costs for the day-to-day things that you need to do well in your studies.
- Free – laptop loans, Wi-Fi and printing
- Free specialist software for your course
- Free online training with LinkedIn Learning.
Financial support
We offer lots of support to help you with fees and living costs. Check out our guide to student life on a budget and find out more about postgraduate funding.
Postgraduate scholarships
Find out more about our postgraduate scholarships including:
- The Alumni Postgraduate Award - for all UK/EU Middlesex alumni a 20% fee reduction
- The Commonwealth Scholarship - full course fees, airfares and a living allowance
- The Chevening Scholarship - full course fees
- The European Academic Awards - £1000 - £7000 for students showing academic excellence
- Regional or International Merit Award -up to £2,000 towards course fees
For international students, we also have a limited number of other awards specific to certain regions, and work in partnership with funding providers in your country to help support you financially with your study.
Help from your employer
Your employer can contribute towards the cost of your postgraduate study as part of their staff development programme.
Work while you study
If you are not currently working, we can help you find work that fits around uni and your other commitments. We have hundreds of student jobs on campus that pay the London Living Wage and above.
Fees disclaimers
1. UK fees: The university reserves the right to increase postgraduate tuition fees in line with changes to legislation, regulation and any government guidance or decisions. The tuition fees for part-time UK study are subject to annual review and we reserve the right to increase the fees each academic year by no more than the level of inflation.
2. International fees: Tuition fees are subject to annual review and we reserve the right to increase the fees each academic year by no more than the level of inflation.
Any annual increase in tuition fees as provided for above will be notified to students at the earliest opportunity in advance of the academic year to which any applicable inflationary rise may apply.
Get answers from our Unibuddy student ambassadors
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We’ll carefully manage any future changes to courses, or the support and other services available to you, if these are necessary because of things like changes to government health and safety advice, or any changes to the law.
Any decisions will be taken in line with both external advice and the University’s Regulations which include information on this.
Our priority will always be to maintain academic standards and quality so that your learning outcomes are not affected by any adjustments that we may have to make.
At all times we’ll aim to keep you well informed of how we may need to respond to changing circumstances, and about support that we’ll provide to you.