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Applied Statistics and Data Science MSc

University of Liverpool

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University of Liverpool

Course options

2 options available

  • Qualification

    Professional Masters

  • Location

    Liverpool Campus

  • Study mode

    Full time

  • Start date

    28-SEP-26

  • Duration

    12 Months

Course summary

Every organisation, in every industry, needs to know how to analyse and translate data into meaningful insights, to drive innovation and positive outcomes. The Applied Statistics and Data Science MSc programme will develop your knowledge and skills to enable you to meet the needs of modern society. The MSc will take you from the foundations of data science, statistical models, and stochastic processes to the mastery of contemporary machine learning methods and programming skills. You will learn how to use powerful statistical and data science methods to create systems capable of extracting compelling insights from big data and predicting outcomes in real-world applications. In semester two, you will develop specialist knowledge in selected areas of applied statistics and data science by choosing one of the following pathways: Global Health and Epidemiology: This pathway will equip you with a unique set of skills to tackle global heath challenges and optimise responses to epidemiological threats. Modules include Infectious Disease Modelling, Spatial and Structural Heterogeneity in Infectious Disease Modelling, and Statistics for Epidemiology. Machine Learning for Investment Science: This pathway will enable you to employ the power of machine learning to understand how to model, predict, and interpret international financial trends and economic forces. Modules include Quantitative Risk Management, Mathematical Finance, and Machine Learning for Finance. Social Finance: This pathway will equip you with the quantitative and analytical skills to address social challenges through innovative financial strategies. You will explore how mathematical tools and financial models can support sustainable development. You will gain practical insight into designing and implementing impactful solutions through entrepreneurial thinking and innovation. Modules include Mathematics of Social Finance, Quantitative Risk Management, and Entrepreneurial Thinking and Innovation Statistics: This pathway will provide you with a broad understanding of the applications of statistical methods and machine learning, while also offering a deep dive into the mathematical methods of data science. (This pathway is only suitable for entrants holding a BSc in Mathematics, Theoretical Physics, or equivalent.) Modules include Machine Learning for Finance, Statistics for Epidemiology, and Stochastic Theory and Methods in Data Science. This exciting and stimulating programme is offered by the Department of Mathematical Sciences and delivered by world-leading experts in their field who are accomplished teachers and researchers, working to tackle real-world problems in epidemiology, financial mathematics, and more! The Department hosts the Mathematics Centre of Enhancement in Education, which supports colleagues to develop innovative teaching methods and ensure that you are taught in the most effective and engaging way. The degree is expected to be accredited by the Institute of Mathematics and its Applications (IMA) and Royal Statistical Society (RSS). Accreditation is pending approval.

Application deadline

11/09/2026

Tuition fees

Students living in United States
(International fees)

£ 2,833month

Tuition fees shown are for indicative purposes and may vary. Please check with the institution for most up to date details.

University information

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University of Liverpool

University of Liverpool

  • University League Table

    22nd

  • Campus address

    Main Site, The Foundation Building, Brownlow Hill, Liverpool, Liverpool, L69 7ZX, United Kingdom

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Subject rankings

  • Subject ranking

    Computer Science

    28th out of 118

    48th out of 65 12

  • Entry standards

    / Max 229
    135 59%

    35th

  • Graduate prospects

    / Max 100
    87.0 87%

    25th

    13

  • Student satisfaction

    / Max 4
    3.09 77%

    47th

    21

  • Entry standards

    / Max 236
    138 59%

    30th

  • Graduate prospects

    / Max 100
    62.0 62%

    61st

    2

  • Student satisfaction

    / Max 4
    3.14 78%

    47th

    5

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