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Statistics with Machine Learning at University of Nottingham - UCAS

Course options

2025 - 2026
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Course summary

Are you considering a career as a professional statistician in industry? Do you want to follow a career in machine learning? This course will provide you with the necessary advanced knowledge and skills. This masters course differs from the Statistics MSc, by providing a particular focus on statistical machine learning to help you understand and build methods that are used in everyday life. For instance, machine learning algorithms are used in mobile phones, self-driving cars, digital health, protein engineering, adaptive clinical trials and lifelong learning in robotics. Well-qualified statisticians and data scientists are in demand world-wide as the amount of digital data we generate increases. Employment opportunities are broad in sectors such as:

  • pharmaceuticals
  • finance
  • computing and AI
  • business analytics
  • healthcare
  • government policy
  • social media and technology
Course structure In the Autumn semester you will study the core fundamentals of statistics and data science. This will provide the basis for the optional modules. This will provide a firm foundation in statistical machine learning for the Spring semester which includes optional modules covering topics in time series and multivariate statistics. You will develop advanced statistical techniques. This will enable you to test theories, learn how to interpret large and complex data, and to extract relevant insights from it. The masters will enable you to develop computing skills in using R too. The course can lead to careers in data science, healthcare and the digital economy. In the summer you'll complete a dissertation. This will be in collaboration with staff from the school's internationally recognised Statistics and Probability Section, or one of our industry partners, such as Capital One. Please note: this is the proposed course structure for 2025, the modules are currently being finalised so please check here or contact the Course Director before submitting your application.

Modules

  • Statistical Foundations,
  • Classical and Bayesian Inference,
  • Statistical Machine Learning,
  • Statistics Dissertation.
Optional modules include:
  • Applied Multivariate Statistics
  • Computational Statistics
  • Machine Learning and Inference for Differential Equations,
  • Stochastic Financial Modelling,
* Time Series and Forecasting.

Assessment method

By Coursework, Dissertation, Examinations, Project work. You will be awarded the Master of Science Degree provided you have successfully completed the taught stage by achieving a weighted average mark of at least 50% with no more than 40 credits below 50% and no more than 20 credits below 40%. You must also achieve a mark of at least 50% in the dissertation.

Entry requirements

* A high 2:2 (55% or international equivalent) Honours degree in mathematics or a closely related subject with substantial mathematical content. A strong mathematical background is essential. * IELTS: 6.0 (no less than 5.5 in each element). * As well as IELTS (listed above), we also accept other English language qualifications. This includes TOEFL iBT, Pearson PTE, GCSE, IB and O level English. * Applicants should have a solid background in mathematics including calculus, linear algebra, probability and statistics at degree level. We may ask you to provide detailed syllabi, including module descriptions, of all mathematics and statistics modules that are a part of your degree. * We accept a wide range of qualifications from all over the world. For information on entry requirements from your country, see our country pages.

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

UK fees: £13,450. International fees: £27,150.
Statistics with Machine Learning at University of Nottingham - UCAS