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Financial Data Science MSc

University of Surrey

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Course options

  • Qualification

    MSc - Master of Science

  • Location

    University of Surrey, Stag Hill - Guildford

  • Study mode

    Full time

  • Start date

    14-SEP-26

  • Duration

    1 Year

Course summary

Data science has become essential in managing many day-to-day aspects of financial markets over the past two decades. Using data sets, banks and financial institutions can unravel all kinds of facts, trends, anomalies and patterns – but only with the right technical skills and understanding. Our cutting-edge course combines artificial intelligence (AI), data science, financial modelling and economic theory, and grounds you in the latest thinking in this area.Taught by expert staff and supported by industrial advisers from financial City institutions, and studying in Guildford – within easy reach of London’s financial hub – the course will greatly enhance your employability in financial and related areas.

Tuition fees

Students living in United States
(International fees)

£ 21,300per year

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

University information

University image

University of Surrey

  • University League Table

    19th

  • Campus address

    University of Surrey, Stag Hill - Guildford, Guildford, Surrey, Guildford, Surrey, GU2 7XH, United Kingdom

Surrey hosts around 140 nationalities. Its renowned international student support team advises on everything, including visas and choosing the right course.
At Surrey, expect a personalised Welcome timetable. This includes international orientation sessions, an induction registration talk and campus tours.
Surrey Research Park's 200+ companies are responsible for 10% of the local economy.

Subject rankings

  • Subject ranking

    29th out of 117 3

  • Entry standards

    / Max 223
    134 60%

    41st

  • Graduate prospects

    / Max 100
    92.0 92%

    18th

    17
  • Student satisfaction

    / Max 4
    3.07 77%

    48th

    4

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