The MSc Statistics is intended for students with a degree in mathematics (or a related subject with a substantial mathematical content). Some prior knowledge of statistics would be helpful to start the course.
The course is designed such that the module 'Fundamentals of Statistics' is either a pre or a co-requisite for all other modules offered in the course. You will study for 180 credits, split across 120 credits worth of taught modules and a 60-credit research project (Statistics Dissertation).
Fundamentals of Statistics
In this module you will explore the fundamental principles and techniques underlying modern statistical modelling and data analysis. The course will cover a 'common core' consisting of statistical concepts and methods, linear models, probability techniques and Markov chains. You will gain experience of using a statistical package and interpreting its output.
In addition, you will study more advanced material concerned with the two main theories of statistical inference, namely classical (frequentist) inference and Bayesian inference. Topics such as sufficiency and best-unbiased estimators are explored in detail. There is special emphasis on the exponential family of distributions. Topics in Bayesian inference include basic ingredients (prior, likelihood and posterior), conjugacy, vague peior knowledge, marginal and predictive inference, decision theory, normal inverse gamma inference, and categorical data.
You will work on a substantial investigation on a topic in statistics or probability. The study will be largely self-directed, although a supervisor will provide oversight and input where necessary. The topic could be based on the statistical analysis of a substantial dataset, an investigation into the statistical methodology or an investigation into a topic of applied probability or probability theory. It is expected that most projects will contain an element of statistical computing.
You may choose a minimum of two of the following:
Applied Multivariate Statistics
This module is concerned with the analysis of multivariate data, in which the response is a vector of random variables rather than a single random variable. A theme running through the module is that of dimension reduction.
Key topics to be covered include:
- principal components analysis, whose purpose is to identify the main modes of variation in a multivariate dataset
- modelling and inference for multivariate data, including multivariate regression data, based on the multivariate normal distribution
- classification of observation vectors into subpopulations using a training sample
- canonical correlation analysis, whose purpose is to identify dependencies between two or more sets of random variables.
Further topics to be covered include methods of clustering and multidimensional scaling.
Applied Statistical Modelling
This module extends the theoretical aspects of statistical inference by developing the theory of the generalised linear model and its practical implementation. Initially, designing of experiments in order to explore relationship between factors and a response is viewed within the context of Linear models.
The module then extends the understanding and application of statistical methodology established in previous courses to the analysis of discrete data and survival, which frequently occur in diverse applications. You will be trained in the use of an appropriate high-level statistical package.
Stochastic Financial Modelling
The aim of the module is to provide an introduction to probabilistic and stochastic modelling for investment strategies, and for the pricing of financial derivatives in risky markets. The probabilistic ideas that underlie the problems of portfolio selection, and of pricing and hedging options, are introduced. You will gain experience of a topic of considerable contemporary importance, both in research and in applications. You will undertake a project which will involve independent reading, and a written report.
Time Series and Forecasting
This module will provide a general introduction to the analysis of data that arise sequentially in time. Several commonly occurring models will be discussed and their properties derived. Methods for model identification for real-time series data will be considered. Techniques for estimating the parameters of a model, assessing its fit and forecasting future values will be developed. You will gain experience of using a statistical package and interpreting its output.
And at least one from this group, but students may not take both Statistical Machine Learning and Data Analysis and Modelling.
This module explores how computers allow the easy implementation of standard, but computationally intensive, statistical methods and also explores their use in the solution of non-standard analytically intractable problems by innovative numerical methods. Particular topics covered include a selection from simulation methods, Markov chain Monte Carlo methods, the bootstrap and nonparametric statistics, statistical image analysis, and wavelets. You will gain experience of using a statistical package and interpreting its output.
Data Analysis and Modelling
The aim of this module is to explore the application of probability and statistics to a variety of practical, open-ended problems. You will study specific projects through workshops and student-led group activities enabling you to develop skills in model development and refinement, report writing and teamwork.
Group project one
- Development of group/communication skills
- Report in the form of a poster and brief presentation
Group project two
- Development of modelling methodology and problem-solving
- Formal report and oral presentation
Group project three
- To display originality in the applications of statistical approaches to a substantial unseen and open-ended problem
- Supported by weekly group meetings
Statistical Machine Learning
This module is a topic at the interface between statistics and computer science, concerning models that can adapt to and make predictions based on data. This module builds on the principles of statistical inference and linear regression to introduce a variety of methods of clustering, dimension reduction, regression and classification.
Much of the focus is on the bias-variance trade-off and on methods to measure and compensate for overfitting. The learning approach is hands-on; you will be using R extensively in studying contemporary statistical machine learning methods, and in applying them to tackle challenging real-world applications.
The above is a sample of the typical modules that we offer but is not intended to be construed and/or relied upon as a definitive list of the modules that will be available in any given year. This course page may be updated over the duration of the course, as modules may change due to developments in the curriculum or in the research interests of staff.
Teaching methods and assessment
The course is taught mostly through lectures, supported with smaller seminar groups which are used to revisit more complex topics.
You will typically be assessed by an examination or coursework at the end of the semester in which a given module is taught.
The MSc will provide you with specific techniques and skills suitable for a professional career in statistics or as a solid basis for research in statistics. There is significant demand for people with postgraduate qualifications in this area.
Some useful information can be found at The Royal Statistical Society website
Average starting salary and career progression
100% of postgraduates in the School of Mathematical Sciences secured work or further study within six months of graduation. The average starting salary was £30,800, with the highest being £60,000.*
* Known destinations of full-time home postgraduates who were available for employment, 2016/17. Salaries are calculated based on the median of those in full-time paid employment within the UK.
Careers support and advice
We offer individual careers support for all postgraduate students whatever your course, mode of study or future career plans.
You can access our Careers and Employability Service during your studies and after you graduate. Expert staff will help you research career options and job vacancies, build your CV or résumé, develop your interview skills and meet employers.
More than 1,500 employers advertise graduate jobs and internships through our online vacancy service. We host regular careers fairs, including specialist fairs for different sectors.
As a student on this course, we do not anticipate any extra significant costs, alongside your tuition fees and living expenses. You should be able to access most of the books you’ll need through our libraries, though you may wish to purchase your own copies which you would need to factor into your budget.
Scholarships and bursaries
School scholarships for UoN UK alumni
For 2020/21 entry, 10% alumni scholarships may be offered to former University of Nottingham graduates who have studied at the UK campus.
Government loans for masters courses
Masters student loans of up to £10,906 are available for taught and research masters courses. Applicants must ordinarily live in the UK or EU.
International and EU students
Masters scholarships are available for international and EU students from a wide variety of countries and areas of study. You must already have an offer to study at Nottingham to apply. Please note closing dates to ensure you apply for your course with enough time.
We provide guidance on funding your degree, living costs and working while you study. You can also access specific funding opportunities, entry requirements and other resources for students from specific countries.