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Data Science at Lancaster University - UCAS

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2025 - 20262026 - 2027
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There are other course options available which may have a different vacancy status or entry requirements – view the full list of options

Course summary

The MSc Data Science has been designed by leading academics and industry experts to deliver an education that matches the needs of employers. The world of data science and AI is rapidly evolving, and so are we. Lancaster University’s Data Science Institute plays a leading role in UK data science research, which means we continually refine and update our curriculum to make sure you are learning the most modern tools and techniques. As demonstrated by our hundreds of alumni, we develop graduates with the skills and expertise to become professionals that design, test and implement solutions to drive innovation and business decision making. You will gain the confidence to apply data science techniques that enable companies to use artificial intelligence to gain insights and make better decisions. This flexible Master’s allows you to select modules, guided by pathways, to develop an enhanced understanding of modern data science technologies. The pathways are:

  • Biodiversity
  • Business Intelligence
  • Data Engineering
Who is this programme for? For those interested in gaining insights from data to provide solutions, this Master’s in Data Science is open to anyone with an undergraduate degree, providing you have a good grasp of statistical analysis, ideally to A level standard. You may be a recent graduate or you may wish for a career change.
  • Looking ahead to employability
The strength of Lancaster’s MSc Data Science is producing graduates with demonstrable experience on their CV. Throughout the course you will network and interact with data science industry professionals through guest lectures, group projects and professional skills workshops. Crucially, you will have the opportunity to work on a real-time project during your 14-week placement with one of our industry partners, choosing techniques and approaches from your data science toolkit to provide answers to their questions. You will also:
  • Gain the confidence to successfully speak the language of data science to future employers and colleagues
  • Develop your programming skills in R and Python
  • Graduate with the ability to think critically, design solutions, build a rationale and communicate this effectively
What to expect Your learning and practical experience is split into three parts.
  • Core data science modules
Study the core modules that span the breadth of data science including the fundamentals of statistics and programming in Python; modern machine learning; and artificial intelligence. This term is essential in providing the foundations for you to advance your knowledge and technical skills in your chosen pathway.
  • Specialist modules
Choose your specialist pathway, selecting optional modules that align with your interests and career goals.
  • Industry placement or dissertation
Apply the knowledge and skills you've gained with a 14-week placement or dissertation, either within industry or as part of an academic research project. Our students really value this experience, with many offered jobs at the end. You will develop your ability to formulate a project plan, gather and analyse data, interpret your results, and present findings in a professional environment. This research will be an opportunity to bring together everything you have learnt over the year, expand your problem-solving abilities and manage a significant project. Three things our students would like you to know
  • The practical elements of this course along with the theory make this like no other!
  • The flexibility of choosing a pathway allows you to focus your career options
  • Working with other people on group projects is so diverse and enhances your project management and communications skills

Modules

Core modules may include: Generalised linear models; data mining; statistical inference; likelihood inference. Optional modules may include: Systems architecture and integration; applied data mining.

Assessment method

Assessment is based on coursework and examination.

Entry requirements

2:2 Hons degree (UK or equivalent) in Statistics, Computer Science or similar. We may also consider non-standard applicants, please contact us for information. We may ask you to provide a recognised English language qualification, dependent upon your nationality and where you have studied previously. We normally require an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 6.0 in each element of the test. We also consider other English language qualifications.

Fees and funding

Tuition fees

No fee information has been provided for this course

Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .

Additional fee information

For information about fees and funding please visit our website: www.lancaster.ac.uk/study/fees-and-funding.
Data Science at Lancaster University - UCAS