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Statistics and Data Science MSc

2 years with professional placement, starting in September 2025

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Develop a strong foundation in the theory and practice of data science and specialist skills in statistics ready for a career in industry.

Data science is vital to the global economy and scientific progress across numerous fields from bioscience, to energy and telecommunications. We'll provide you with the practical tools and mathematical foundations to standout to industry experts.

Register your details to watch our short taster lectures and get a taste of the high-quality teaching on our maths courses.

Who is this course for?

This course is designed for graduates in highly numerate disciplines who would like to build on a foundational knowledge of statistics and develop skills in machine learning and data science. It provides training in programming, machine learning and applied statistics that will give you the skills and knowledge to challenge real-world data using more advanced analytical techniques.

Course highlights

  • Learn strong statistical foundations of universal relevance.
  • Gain expertise in statistical and probabilistic modelling, including the design, collection, analysis and presentation of data.
  • Study the mathematical theory and practical implementation of modern data science and machine learning techniques.
  • Undertake an optional 12-month placement with a company or public sector body to improve your employability.
  • Be part of our supportive postgraduate community.
  • Live and study in a beautiful world heritage city.

Summer Research Project

The research expertise in the department allows for a wide-range of subjects for your final project at the end of the course. Recent examples include:

  • Deep learning in high frequency financial trading
  • Spatiotemporal timing prediction for public transport
  • Wastewater-based epidemiology

Career prospects

After graduating, you'll be well placed for a variety of careers in data science - from large-scale commercial enterprises, to innovative tech start-ups. Alongside the specialist skills and knowledge you'll gain, our dedicated careers team offers individual guidance and help you decide between employment and further study.

Recent graduate roles include data scientists and machine learning developers.

Find out more on maximising your employability while at Bath.

Find out more about studying at Bath

2025/26 Academic Year

Before you apply for a course, please check the website for the most recently published course detail. If you apply to the University of Bath, you will be advised of any significant changes to the advertised programme, in accordance with our Terms and Conditions.

We understand that you will want to know more about the shape of the academic year. We work hard and plan for different scenarios, to be able to welcome you to the University of Bath at the start of each semester.

Course structure

This course lasts 2 years. It starts in September 2025 and ends in 2027. Welcome week starts on 22 September 2025.

Occasionally we make changes to our programmes in response to, for example, feedback from students, developments in research and the field of studies, and the requirements of accrediting bodies. You will be advised of any significant changes to the advertised programme, in accordance with our Terms and Conditions.

Year 1

Semester 1

Compulsory units

  • Applied data science

    10 credits

  • Foundations and applications of machine learning

    10 credits

  • Programming for data science

    10 credits

  • Statistical modelling

    5 credits

  • Statistics for data science

    5 credits

Semester 2

Compulsory units

  • Applied data science

    Continued

  • Applied statistics

    5 credits

  • Bayesian data analysis

    5 credits

  • Foundations and applications of machine learning

    Continued

  • Research project preparation

    5 credits

  • Statistical design of investigations

    5 credits

Summer

Compulsory units

  • Dissertation

    30 credits

Year 2

Semester 1

Compulsory units

  • Professional placement

    60 credits

Semester 2

Compulsory units

  • Professional placement

    Continued

Placement

Going on placement gives you the opportunity to apply your skills and knowledge working in industry. You’ll be employed full-time in a role to match your future career ambitions, broadening your experience and transferable skills.

We have links with companies of all sizes from household names to start-ups. Recent placements include positions at EDF, Intel, AtomX Ditigal and Infomentum.

Placement opportunities can't be guaranteed but you will receive tailored support from our specialist team to help you secure a placement.

Find out more about going on placement

Learning and assessment

You’ll be taught and assessed by a variety of methods and it will vary between units. These methods are designed to promote in-depth learning and understanding of the subject.

Learning

  • Lectures
  • Online resources
  • Practical sessions
  • Seminars
  • Tutorials

Assessment

  • Coursework
  • Essay
  • Multiple choice examination
  • Online assessment
  • Practical work
  • Thesis
  • Written examination
  • Other

These lists are to give you an idea of some, but not all, of the learning and assessment methods used on this course. They are not exhaustive lists and methods are subject to change.

Learning and teaching

Overall workload

You should expect to spend approximately 35 to 40 hours a week studying on your course. These hours consist of structured activities and independent learning. You will experience a mix of in person teaching, that will take place on campus, and structured online learning delivered through the University’s virtual learning environment.

Structured learning activities

MSc programmes deliver taught units in the first and second semesters, with the Summer dedicated to a dissertation. The second semester will have less structured learning as you will start to prepare for your dissertation. Both during the dissertation preparation and the dissertation, supervision will take place at times suitable to you and your supervisor.

Typically, you can expect to spend approximately 12 hours per teaching week on structured learning activities. The majority of these activities will be in timetabled sessions on campus. A small proportion may be online.

In-person teaching and online activities

Timetabled sessions delivered in person on campus will be a mix of lectures, seminars, tutorials, and laboratories. Online activities may include following a recorded lecture, or other learning materials, or joining a timetabled live interactive session through Microsoft Teams or Zoom.

Independent learning

The remainder of your time outside these structured activities will be spent in independent learning which includes individual research, reading journal articles and books, working on individual and group projects, preparing coursework assignments, presentations, or revising for exams.

To support you in your studies you will be able to access, outside of timetabled learning, facilities on campus and in Bath such as study spaces, computers, and the Library.

Recognition of professional qualifications

As well as being recognised as a higher academic qualification, a number of our degrees are also accredited by professional bodies in the United Kingdom. An accredited degree may entitle you to work in a specific profession within the UK, and abroad (where there are reciprocating arrangements with professional bodies in other countries).

The requirements to practise a profession vary from country to country. If you wish to practise your profession outside the United Kingdom, you are advised to confirm that the UK professional qualification you seek is valid in the country in which you are intending to work.

Entry requirements

Origin of qualifications

British qualifications

You should have a first or strong second-class undergraduate degree or equivalent international equivalent.

To apply for this course, your undergraduate degree should be in a subject that incorporates a substantial mathematical element such as mathematics, statistics, computer science, physics, chemistry, engineering or economics. Experience of computer programming would also be advantageous.

We will also consider other subjects, for example geography or biology, which may meet the criteria depending on their specific mathematical and computing content.

We may make an offer based on a lower grade if you can provide evidence of your suitability for the degree.

If your first language is not English but within the last 2 years you completed your degree in the UK you may be exempt from our English language requirements.

English language requirements

  • IELTS: 6.5 overall with no less than 6.0 in all components
  • The Pearson Test of English Academic (PTE Academic): 62 with no less than 59 in any element
  • TOEFL IBT: 90 overall with a minimum 21 in all 4 components

You will need to get your English language qualification within 24 months prior to starting your course.

If you need to improve your English language skills before starting your studies, you may be able to take a pre-sessional course to reach the required level.

Fees and funding

Fees and funding information for Statistics and Data Science MSc with professional placement

Fees

Your tuition fees and how you pay them will depend on whether you are a Home or Overseas student.

Learn how we decide fee status

Tuition fees

See the most recent fees for postgraduate courses.

Placement fees

You will normally pay a reduced tuition fee while on a placement period or studying abroad. Find out more about placement fees and study abroad fees.

Extra costs

If you receive an offer, you will need to pay a non-refundable deposit of £1,000 when you accept to secure your place. This will be deducted from your tuition fee when you register.

IT requirements

Computational work will be an integral part of the course. Therefore, you are strongly encouraged to get up to speed with Python before starting the MSc and to bring a laptop computer with the Windows, Mac or Linux operating system and an up-to-date Python installation.

How to pay

Tuition fee loans

If you are studying a postgraduate course, you may be able to take out a loan for your tuition fees and living costs.

Read more about tuition fee loans

Scholarships and bursaries

You could be considered for a bursary or scholarship to help you study at Bath. You do not have to pay it back.

Read more about bursaries and scholarships

Other payment options

You can pay your tuition fees by Direct Debit, debit card, credit card or bank transfer. You may also be eligible for a student loan to help you pay your fees.

Read more about your payment options

Budgeting

You will need to budget at least £100 for the cost of photocopying, printing and binding. You will also need to budget for the cost of textbooks.

Some courses involve visits away from campus and you may be required to pay some or all of the costs of travel, accommodation and food and drink.

If you’re on a placement, you’re responsible for your own travel, accommodation and living costs. You should also consider the financial implications if you go on an unpaid or overseas placement.

Application information

  • Course title
    Statistics and Data Science
  • Final award
    MSc
  • Mode of study
    Full-time
  • Course code
    TDUCM-DS10
  • Department
    Department of Mathematical Sciences
  • Location
    University of Bath
    Claverton Down, Bath BA2 7AY
  • Home application deadline
    31 August 2025

    We recommend you apply early as we may close applications before the deadline if a course is full.

  • Overseas application deadline
    31 July 2025

    We recommend you apply early as we may close applications before the deadline if a course is full. We may consider late applications but if you need a Student Visa to study in the UK, you will need time to apply for and receive your visa to be in the UK by the start of the course.

  • Application eligibility
    Home and Overseas students are eligible to apply
  • Regulator
    The Office for Students (OfS)

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