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Data Science MSc

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

Data Science MSc

September 2024
1 year full-time
2 years part-time
September 2024: £11,000 (UK)* £17,300 (INT)*
Course leader
Giovanni Quattrone
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All industries now utilise data and Data-Science and Data-Analytics are increasingly identified as key 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.

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.

3 great reasons to pick this course

  • Strong career paths
    Graduates have gone on to work for companies such as Swiss Re and Norton Rose Fulbright and have also gone on to found their own companies
  • Cutting-edge facilities
    You will have access to our state-of-the-art problem-solving rooms that mimic real-life software development team working
  • Hands-on learning
    Gain experience of analysing real life data sets and a theoretical understanding of underlining data science techniques.

Find out more

Sign up now to receive more information about studying at Middlesex University London.

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.

  • Modules

    • Modelling, Regression and Machine Learning (30 credits) - Compulsory

      This course will equip you with the theoretical and algorithmic basis for understanding learning systems and the associated issues with very large datasets/data dimensionalities. You will be introduced to algorithmic approaches to learning from exemplar data and will learn the process of representing training data within appropriate feature spaces for the purposes of classification. You will also focus on basic data structures and algorithms for efficient data storage and manipulation. The major classifier types are taught before introducing the specific instances of classifiers along with appropriate training protocols. You will explore where classifiers have a relationship to statistical theory as well as notions of structural risk with respect to model fitting. You will be equipped with techniques for managing this in practical contexts.

    • Visual Data Analysis (30 credits) - Compulsory

      This module provides an understanding of the methods, theories and techniques relevant to interactive visual data analysis. You will learn relevant principles and practices in visual data analysis design, implementation, and evaluation. You will gain experience in researching, designing, implementing, and evaluating your own visual analysis solutions, using both off-the-shelf tool-kits and data visualisation programming libraries. You will gain the knowledge to support your future employment or research in the fast-developing areas of data science, particularly visual analytics.

    • Applied Data Analytics: Tools, Practical Big Data Handling, Cloud Distribution (30 credits) - Compulsory

      This course will provide an in-depth of the tools and systems used for mining massive dataset and, more in general, an introduction to the fascinating emerging field of Data Science. The module is divided in two parts: The first part focuses on the languages Python and R, a statistical learning language used to learn from data. This part provides an overview of the most common data mining and machine learning algorithms and every discussed concept is accompanied by illustrative examples written in Python and R languages. The second part of the module takes a tour through cloud computing and big data systems and teaches the participant how to effectively use them. Specifically, platforms and systems like OpenStack, Hadoop, MapReduce, MongoDB, Spark and NoSQL databases are introduced and every concept is accompanied by a number of illustrative examples.

    • Legal, Ethical and Security Aspects of Data Management (30 credits) - Compulsory

      This module focuses on legal, ethical and security requirements that underpin the technical processes and practice of data science (the collection, preparation, management, analysis and interpreting of large amounts of data called big data). Data science leads to predictive analyses and insights into big data for businesses, healthcare organisations, governments and security services among others. The volume of data collected, stored and processed brings many concerns especially related to privacy, data protection, liability, ownership and licensing of intellectual property rights and information security. This module will explore how data can be fairly and lawfully processed and protected by legal and technical means. It will give students a comprehensive understanding of important legal domains/regulatory issues, relevant ethical theories/guidance and important information security management policies that impact on the practice of data science. Further it will equip student with the necessary foundations to develop high professional standards when working as data scientists.

    • Individual Data Science Project (60 credits) - Compulsory

      The project module aims to develop your knowledge and skills required for planning and executing research projects such as proof of concept projects or empirical studies related to data science. To plan and carry out your projects you will have to:

      • Apply theories, methods and techniques previously learned.
      • Critically analyse and evaluate research results drawing on knowledge from other modules.
      • Develop your communication skills to enable you to communicate your findings competently in written and oral form.

To find out more about this course, please download the Data Science MSc specification (PDF).

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.

Where will I study?

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.

Teaching vs independent study

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:


Hours per week



Independent study


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.

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 over two years.

Academic support

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.

Coursework and assessments

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.


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.


The Sheppard Library

Our library is open 24 hours a day during the term and includes:

  • Over 1,000 study areas with rooms for group study and over 600 computer spaces
  • 350,000 books and e-books and more than 24,000 online journals
  • Free laptop loans, Wi-Fi and printing.

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.


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.

Financial support

You can apply for scholarships and bursaries and our MDX Student Starter Kit to help with up to £1,000 of goods, including a new laptop or iPad.

We have also reduced the costs of studying with free laptop loans, free learning resources and discounts to save money on everyday things. Check out our guide to student life on a budget.

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.

95% of our postgraduate students go on to work or further study (Graduate Outcomes 2022).


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.

  1. UK entry
  2. International entry
  3. How to apply

The fees below are for the 2024/25 academic year:

UK students1

Full-time students: £11,000
Part-time students: £73 per credit
Part-time students: £37 per dissertation credit

International students2

Full-time students: £17,300
Part-time students: £117 per credit
Part-time students: £59 per dissertation credit

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

You may be eligible for one of our 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 to £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.

Find out more about our postgraduate scholarships.

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.

Dr David Windridge
Associate Professor in Computer Science

Dr Windridge heads the University's Data Science activities. His research interests centre on the related fields of machine learning, cognitive systems and computer vision. He also has a former research interest in Astrophysics.

He has played a leading role on a number of large-scale machine-learning projects in academic and industrial research and has won a number of interdisciplinary data science research grants areas such as psychological modelling and proteomics. He has authored more than 100 peer-reviewed publications (including best paper awards), with over 1000 citations collectively.

Dr Giovanni Quattrone
Senior Lecturer in Computer Science

Dr Quattrone is a prominent researcher and expert in the fields of Social Data Science and Urban Science. With a strong background in data-driven analysis and interdisciplinary research, Dr Quattrone has made significant contributions to advancing our understanding of complex social phenomena and urban dynamics.

Dr Quattrone's expertise lies in leveraging computational methods, such as machine learning and network analysis to gain insights into social interactions and human behaviour across both online and offline domains. Dr. Quattrone's research also encompasses the analysis of urban data, driving the development of data-driven solutions to enhance urban planning, sustainability, and livability.

To date, Dr Quattrone has published more than 80 peer-reviewed publications (including best paper awards), with over 1800 citations collectively (Source: Google Scholar).

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.

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