Financial Mathematics & Data Science 12 months Postgraduate Programme By University of Strathclyde |TopUniversities
Subject Ranking

# 251-300QS Subject Rankings

Programme Duration

12 monthsProgramme duration

Main Subject Area

MathematicsMain Subject Area

Programme overview

Main Subject

Mathematics

Degree

MSc

Study Level

Masters

Study Mode

On Campus

Our MSc Financial Mathematics & Data Science is an advanced course that offers you the opportunity to develop theoretical and practical skills in financial theory, statistics and data science.

You'll be supported by expert members of staff across three departments to gain the knowledge and skills required to apply quantitative techniques in a financial context. Following completion of this course, you will be able to:

  • explain key concepts in financial theory and data science
  • apply key methods in data science to financial contexts (for example, machine learning, numerical methods, stochastic processes)
  • apply knowledge and problem-solving skills in new or unfamiliar situations, including in multidisciplinary contexts
  • handle complex situations, and formulate judgements with incomplete or limited information
  • interpret the output of models and analysis, and explain how these relate to real life
  • use the R and Python programming packages to carry out analysis
  • communicate results to specialist and non-specialist audiences, both in written reports and in presentations

Programme overview

Main Subject

Mathematics

Degree

MSc

Study Level

Masters

Study Mode

On Campus

Our MSc Financial Mathematics & Data Science is an advanced course that offers you the opportunity to develop theoretical and practical skills in financial theory, statistics and data science.

You'll be supported by expert members of staff across three departments to gain the knowledge and skills required to apply quantitative techniques in a financial context. Following completion of this course, you will be able to:

  • explain key concepts in financial theory and data science
  • apply key methods in data science to financial contexts (for example, machine learning, numerical methods, stochastic processes)
  • apply knowledge and problem-solving skills in new or unfamiliar situations, including in multidisciplinary contexts
  • handle complex situations, and formulate judgements with incomplete or limited information
  • interpret the output of models and analysis, and explain how these relate to real life
  • use the R and Python programming packages to carry out analysis
  • communicate results to specialist and non-specialist audiences, both in written reports and in presentations

Admission Requirements

12 Months
Sep

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