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Students
Tuition Fee
GBP 26,350
Per year
Start Date
Medium of studying
Duration
12 months
Program Facts
Program Details
Degree
Masters
Major
Mathematical (Theoretical) Statistics | Probability Theory | Statistics
Area of study
Mathematics and Statistics
Course Language
English
Tuition Fee
Average International Tuition Fee
GBP 26,350
About Program

Program Overview


The MSc Statistics with Financial Mathematics program at the University of Sheffield equips students with probabilistic, statistical, and mathematical skills for a career in finance. Graduates are prepared for high-demand roles in banking, actuarial work, and insurance, and the program is accredited by the Royal Statistical Society.

Program Outline


Degree Overview:

The MSc Statistics with Financial Mathematics program at the University of Sheffield is designed to equip students with the necessary probabilistic, statistical, and mathematical skills for a successful career in the finance industry. The program builds upon the foundation of the Statistics MSc, incorporating key financial topics such as the Capital Asset Pricing Model, the Black-Scholes option pricing formula, and stochastic processes.


Objectives:

  • Develop a strong understanding of the mathematical concepts, models, and tools used in the finance industry.
  • Gain proficiency in applying probabilistic, statistical, and mathematical techniques to real-world financial problems.
  • Enhance programming skills using the statistical computing software R.
  • Develop research skills through a dissertation project, potentially focusing on a data set or a theoretical/methodological topic.
  • Gain experience in planning, researching, data acquisition, problem specification, analysis, and reporting findings.

Outline:

The program is structured as follows:


Full-time Residential:

1 year


Part-time Distance Learning:

2 or 3 years


Core Modules:

  • Financial Mathematics (15 credits): This module explores the mathematical ideas behind the Capital Asset Pricing Model and the Black-Scholes option pricing formula, along with their applications in modern finance.
  • It includes a project component.
  • The Statistician's Toolkit (30 credits): This module prepares students for the workplace by equipping them with essential statistical modeling, computing, and professional skills.
  • It covers linear and generalized linear modeling, and data analysis using the R programming language.
  • Bayesian Statistics and Computational Methods (30 credits): This module introduces the Bayesian approach to statistical inference, contrasting it with conventional frequentist/classical inference.
  • It also presents computational methods for implementing both Bayesian and frequentist inference, using the R and Stan programming languages.
  • Stochastic Processes and Finance (30 credits): This module examines stochastic processes, models that reflect unpredictable real-world behavior.
  • It applies this knowledge to mathematical finance, focusing on arbitrage-free pricing and the Black-Scholes model.
  • Dissertation (60 credits): The dissertation is an extensive study that allows students to synthesize theoretical knowledge with practical skills.
  • It involves planning, researching, data acquisition, problem specification, analysis, and reporting findings. Most dissertations involve the investigation of a data set, including a description of the relevant background and a report on the data analysis.

Optional Modules (15 credits):

  • Machine Learning (15 credits): This module explores the intersection of computer science and statistics, focusing on developing tools for modeling and understanding complex data sets.
  • Time Series (15 credits): This module focuses on analyzing data collected repeatedly over time, such as daily temperature recordings.
  • It covers specialized methods for analyzing such data, accounting for the relationship between successive observations.

Module Breakdown by Year:


Year 1 (Full-time/Part-time):

  • Core Modules: Financial Mathematics, The Statistician's Toolkit
  • Optional Modules: Machine Learning, Time Series (choose one)

Year 2 (Part-time):

  • Core Modules: Bayesian Statistics and Computational Methods, Stochastic Processes and Finance

Year 3 (Part-time):

  • Core Module: Dissertation
  • Optional Modules: Time Series, Machine Learning (choose one)

Assessment:

  • Most taught modules are assessed through a combination of examinations and coursework.
  • Some modules may be assessed continuously through ongoing project work without examinations.
  • The dissertation module is assessed solely based on the submitted dissertation.

Teaching:

  • The program utilizes a variety of teaching methods, including lectures, tutorials, computing sessions, and group work.
  • Most statistics lectures are recorded for students to review later.

Careers:

  • The program prepares graduates for roles in the finance sector, such as banking, actuarial work, pensions, and insurance.
  • Graduates have secured positions at companies like Goldman Sachs, Lloyds, Aviva, AXA, Amazon, BAE Systems, Deloitte, PwC, and the NHS.
  • The degree satisfies the eligibility criteria for the Royal Statistical Society’s Graduate Statistician award, a stepping-stone to full professional membership of the RSS and Chartered Statistician status.

Other:

  • The program is accredited by the Royal Statistical Society.
  • The School of Mathematical and Physical Sciences provides a supportive community for students.
  • The School has strong links with various professional organizations, including the London Mathematical Society, the Society for Industrial and Applied Mathematics, the Institute of Mathematics and its Applications, the European Physical Society, and the International Society on General Relativity and Gravitation.

Note:

The program content is reviewed annually to ensure its relevance and currency. Individual modules may be updated or withdrawn based on research discoveries, funding changes, professional accreditation requirements, student or employer feedback, review outcomes, and variations in staff or student numbers. Students will be informed of any changes in a timely manner.


Home (2024 annual fee): £12,070 Overseas (2024 annual fee) : £26,350

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Admission Requirements

Entry Requirements:

Minimum 2:1 undergraduate honours degree, with substantial mathematical and statistical components. In particular, you should have studied the following topics and performed well in assessments on them (for example, a score of at least 60 per cent).

  • Mathematical Methods for Statistics: ideas and techniques from real analysis and linear algebra, including multiple integration, differentiation, matrix algebra, the theory of quadratic forms.
  • Probability and Probability Distributions: the laws of probability and of conditional probability, the concepts of random variables and random vectors and their distributions, the methodology for calculating with them; laws of large numbers and central limit phenomena.
  • Basic Statistics: statistical inference, rational decision-making under uncertainty, and how they may be applied in a wide range of practical circumstances; relevant software, for example, R.
  • Real analysis and stochastic processes: limits of sequences and series, convergence tests, continuity and differentiability, stochastic processes and the Markov property.

Language Proficiency Requirements:

Overall IELTS score of 6.5 with a minimum of 6.0 in each component, or equivalent.

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