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Data Science and Analytics MSc Online

Turn big data into even bigger insights and gain the skills to drive your data analytics career forward on this 100% online degree.

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Start Dates: January, May, September

Duration: 1 year full-time, 2 years part-time

Study Mode: Online

Fees: £11,520 (regional discounts available)

About the programme

Global data is growing at rapid speed and so is the need for professionals who can cut through the noise. Unlock the power of data with our online MSc in Data Science and Analytics. Blending hands-on practical training with academic theory taught by some of the world’s leading academics, you’ll be equipped with the skills to turn complex datasets into insights that drive innovation across sectors including retail, finance, healthcare, tech and government.

From day 1, you’ll work with cutting-edge technologies like Hadoop, Spark, Tableau, Python and R and acquire skills in quantitative analysis, data visualisation, big data processing, machine learning and more, leaving you ready to make an impact and shape your future in an increasingly data-driven world.

How you’ll study

Online learning environment

The MSc Data Science and Analytics is delivered through Brunel’s virtual learning environment, giving you access to course materials, recorded lectures, and interactive resources at any time. You will engage with content through video lectures, discussion forums, and collaborative activities with your global cohort. All learning is asynchronous by default, allowing you to study when it suits you, with scheduled touchpoints for tutor interaction and peer discussion.

Weekly study commitment

Full-time students should expect to dedicate approximately 35-40 hours per week to their studies. Part-time students typically commit around 15-20 hours. Your time is split between directed learning activities, independent study and reading, and assessment preparation. The programme runs across three intakes per year – January, May, and September – with a structured module timetable.

Assessment methods

Assessment is through a balance of coursework and examinations. Depending on the module, you may complete written reports, data modelling exercises, machine learning projects, statistical analyses, and in-class tests covering specific technical subjects. The nine-module programme culminates in a dissertation project, where you undertake independent research on a data science topic of your choice. All assessments are completed online.

Support and feedback

You will have access to dedicated academic tutors and a student support team throughout your programme. Tutors provide structured feedback on assessments, and you can reach out to your support team for guidance on academic, technical, or administrative matters. Brunel’s online students also have access to the university’s library, careers service, and wellbeing resources.

Entry requirements

Academic requirements

To join this programme, you’ll need:

  • A 2:2 (or above) UK Honours degree or; an equivalent internationally recognised qualification in a relevant subject for the programme which you are applying.

Language requirements

All modules and assessments are delivered in English, so you’ll need to meet our English language requirements. We accept a range of qualifications, including:

  • IELTS: 6.5 overall (minimum 6.0 in all areas)
  • Pearson (PTE): 59 overall (59 in all subscores)
  • BrunELT: 63% overall (minimum 58% in all areas)
  • TOEFL iBT: 90 overall (minimum 20 in each section)

Modules

All learning is delivered online to fit around your work and personal commitments. You’ll complete eight core modules and a final dissertation, combining theory and practice to prepare you for a career as a data expert.

This module teaches you how to design and implement methods to prepare data using advanced statistical techniques. You’ll then be able to apply these methods to real data to generate insight and design your own data management frameworks.

This module gives you the opportunity to evaluate data quality, critically compare current statistical analysis methods for data exploration and create your own tailored analysis plans for data preparation, exploration and management.

How do you turn data into valuable insights? On this module, you’ll learn how to design and implement effective visualisation methods, algorithms and techniques to generate value and insight from data, leaving you ready to critically reflect on current data visualisation methods. 

Bring your knowledge to life through your very own research project. Choose an area of study, formulate research in response to a problem area, using your critical thinking, evaluation and practical skills to shape your research. 

What can we learn about digital information from the way it’s handled? This module identifies what existing research has to say about the governance of digital information. You’ll consider the legal, social and ethical concerns of using digital systems and critically reflect on the implications of adopting digital technologies in the modern working environment. 

This module helps you develop the skills necessary to extract value and insight from diverse data sets using statistical learning. As you explore machine learning techniques and approaches to performance evaluation, you’ll learn how to evaluate not just their ability to deliver accurate predictions but also the value and limitations of prediction.

This module gives you the opportunity to reflect on the concepts, theory and appropriate use of large-scale storage and processing infrastructures, before designing and planning your own solutions. You’ll also review, design and plan various analytic methods, techniques and algorithms used to generate insight from data, before reflecting on their ability to deliver accurate predictions.

This module equips you with an understanding of innovation in digital business. You’ll gain the skills needed to implement digital business improvement and change while critically evaluating theory and practice. 

Demonstrate the knowledge and skills you’ve gained on this degree programme through a dissertation. You’ll propose, present and evaluate the solution to a research problem or question within a specific field of study, applying the appropriate research and data collection methods to form a complete report. 
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Fees and funding

Tuition fees for the MSc Data Science and Analytics are £11,520 for the full programme. This covers all teaching, learning materials, assessments, and access to Brunel’s online resources – there are no hidden costs.

Payment options

Brunel offers a six-month payment plan, allowing you to spread the cost by paying your first instalment at registration and the remaining balance across five equal monthly payments.

Discounts and scholarships

You may be eligible for one or more of the following:

  • Early Payment Discount (2%) — applied automatically when you pay in full before your registration date
  • Alumni Discount (15%) — for Brunel graduates returning for postgraduate study
  • Family Discount (10%) — when a direct family member is also studying at Brunel
  • Academic Excellence Award (15%) — for applicants holding a first-class undergraduate degree

Discounts can be combined where eligible. Visit the full fees and funding section for detailed eligibility criteria.

Student loans

UK residents may be eligible for a Postgraduate Master’s Loan through Student Finance England. The loan is not means-tested and is paid directly to you to use towards tuition fees and living costs. Visit gov.uk/masters-loan for full details and to check your eligibility.

Career outcomes 

A sector defined by growth

Data science is one of the fastest-growing professional fields globally. Organisations across every industry, from financial services and healthcare to retail and government, are investing in data capability. The demand for professionals who can collect, analyse, and derive strategic insight from data continues to accelerate, with salaries reflecting this scarcity of talent.

Roles our graduates pursue

  • Data scientist – building models and extracting insights from complex datasets
  • Analytics consultant – advising organisations on data strategy and performance measurement
  • Business analyst – translating data into actionable business recommendations
  • Clinical data scientist – applying data science methods to healthcare and pharmaceutical research
  • Quantitative analyst – developing statistical models for financial risk and investment analysis
  • Enterprise architect – designing data infrastructure and analytics systems at scale

Where our graduates work

Brunel Data Science graduates work with leading data-driven organisations including Accenture, AstraZeneca, AXA Insurance, British Airways, PayPal, Experian, Capgemini, FICO, GE Healthcare, and HSBC. The programme’s applied focus prepares you for roles across technology, finance, healthcare, consulting, and the public sector.

A versatile qualification

Data science skills are transferable across industries. Whether you work in healthcare, finance, marketing, logistics, or government, the ability to work with data, build models, and communicate insights is valued. This versatility gives you career flexibility that few other qualifications offer.

Power your future in data

Global excellence: Join a top-ranking department led by faculty who are recognised among the world’s top 2% of scientists. 

Industry-relevant curriculum: Designed with input from industry professionals, learn the skills employers need.

World-class technical training: Put theory to practice with tools including Hadoop, Spark, Tableau, R, Python, and SAS.

Tailored academic support: Get access to mentoring, guidance and employability services from day 1.

Why choose this programme

APHEA-validated curriculum

The Master of Public Health is validated by the Agency for Public Health Education Accreditation (APHEA), confirming it meets international standards for public health education. APHEA validation is a mark of quality recognised by employers, governments, and health institutions across Europe and beyond.

The Generalist Public Health Qualification

The MPH is the most widely recognised postgraduate qualification in public health. It provides broad, multidisciplinary training across epidemiology, health economics, behaviour change, leadership, and research methods — preparing you for senior roles across the full spectrum of public health practice.

Public Health Leadership

A dedicated Public Health Leadership module sets this programme apart from the specialist MSc pathways. You will develop the strategic and management skills needed to lead public health teams, programmes, and organisations.

A London university with global standing

Brunel University London is ranked in the top 25% of UK universities. An MPH from a respected London institution carries weight with NHS trusts, government agencies, and international health organisations worldwide.

Built for working professionals

Three start dates per year (January, May, September), fully online delivery, and flexible study options mean you can advance your public health career without stepping away from your current role.

Career-driven outcomes

Your MPH is designed with industry skills and knowledge in mind, providing you with the skills you need to secure your chosen career path. Each module is carefully constructed to elevate your knowledge in key areas of healthcare relevant to industry standards. 

In-demand data skills that drive innovation

Data science 

Explore key concepts from the disciplines that define modern data science. 

Data analysis

Learn how to turn a critical understanding of data into clear insights for industry innovation.

Strategic tools

Apply data science tools and techniques to problems in social, business and scientific domains.

Digital problem-solving

Address industry challenges with applied data science and evaluate its effectiveness. 

Technical research

Conduct, report on and evaluate research related to problems and challenges in data science.

Professional agility

Demonstrate professional competence in data science and analytics.

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Unlock opportunities in data

Led by industry-leading academics, this degree programme prepares you for a rewarding career with leading employers like Accenture, AstraZeneca, AXA Insurance, British Airways, PayPal, Experian, Capgemini, FICO, GE Healthcare, HSBC and more.

As a Brunel online graduate, you’ll be prepared to step into roles including:

  • Data scientist
  • Analytics consultant
  • Business analyst
  • Clinical data scientist
  • Forecast analyst
  • Customer insight analyst
  • Quantitative analyst 
  • Developer 
  • Enterprise architect 
  • Web analyst

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Fill out the form and one of our advisors will be in touch with more information, including our brochure.

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FAQs

Yes. The MSc Data Science and Analytics is delivered entirely online, giving you flexible access to all learning materials at any time. You can study full-time over one year or part-time over two years. The programme is designed for working professionals, with three start dates per year.

Yes. You will graduate with the same Brunel University London MSc as on-campus students. Your degree certificate does not distinguish between online and on-campus delivery. You are a full Brunel student with access to the university's academic resources, library, and support services throughout your studies.

UK residents may be eligible for a Postgraduate Master's Loan through Student Finance England, which can be used towards tuition fees and living costs. The loan is not means-tested. Brunel also offers a six-month payment plan, along with discounts for alumni, early payment, academic excellence, and family members of current students.

You will need a 2:2 UK Honours degree (or international equivalent) in a scientific, engineering, or numerate subject. A computing or data science background is not required, but you should be comfortable with quantitative and analytical thinking. The programme builds your data science skills progressively from foundational analysis through to machine learning and high-performance computing.

The programme covers data visualisation, machine learning, high-performance computational infrastructures, and digital innovation. You will work with industry-standard data science tools and techniques through a combination of coursework, modelling exercises, and a dissertation project. All required software is accessible through the university's online learning environment.

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