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Study mode

Full time

Duration

1 year

Start date

OCT-26

Key information
DATA SOURCE : IDP Connect

Qualification type

MSc - Master of Science

Subject areas

Applied Statistics

Course Summary

This applied statistics course is ideal if you are considering a career move into statistics, or if your work already involves aspects of data collection and exploration, interpretation of statistics, or modelling and forecasting time-dependent phenomena. It delivers a strong theoretical background but is also practically oriented to develop your ability to tackle new and non-standard problems with confidence. Why choose this course? This course offers you a comprehensive curriculum covering frequentist and Bayesian methods, statistical machine learning and advanced computational techniques. It has a favourable staff-student ratio to ensure high-quality teaching and support, with time for one-on-one consultation as needed. It is accredited by the Royal Statistical Society (RSS), so that our graduates may be eligible to attain Graduate Statistician (GradStat) status. It benefits from being led by experienced statisticians and mathematicians with active research interests in theoretical and applied statistics. What you will learn We emphasise the mutual dependence of practice and theory throughout the course, with a focus on hands-on application through real-world datasets to equip you with the skills to analyse and interpret complex data and communicate findings effectively. You will learn to formulate real-world problems as statistical models and implement them using statistical software. You will also gain a solid grounding in core statistical methodologies, including: regression ANOVA generalised linear models along with an introduction to Bayesian modelling a wide variety of advanced computational statistics and machine learning methods. How you will learn Teaching on this course is through a combination of lectures (pre-recorded), seminars and practical computing sessions. In lectures an overview of the topic engages you with the material through theory, worked problems and example applications. Seminars are focused around discussion and problem-solving while computing sessions allow you to gain practical experience in the analysis and modelling of data. This course is available to study full- or part-time. It has an evening timetable with classes taking place in the evening, so that you can fit your studies in around other work or family commitments. You will also be supported by comprehensive resources, including a dedicated subject librarian and high-quality recordings of lectures. We offer this course as a Master’s and a Postgraduate Certificate. For the Certificate, you study fewer modules and do not complete a dissertation.

Modules

This module provides a solid grounding in the fundamentals of random variables and their distributions, together with an introduction to axiomatic probability theory and the convergence of sequences and sums of random variables. These form the foundations of statistics. We will then discuss the theory underlying modern statistics as well as (mathematical) statistics and the principles of statistical inference. Emphasis is placed on demonstrating the applicability of the theory and techniques in practical applications.

Tuition fees

UK fees
Course fees for UK students

For this course (per year)

£12,000

International fees
Course fees for EU and international students

For this course (per year)

£24,390

Entry requirements

A second-class honours degree (2:2) or above, with mathematics or statistics as a main subject.

University information

Birkbeck, University of London is located in Bloomsbury, in the very centre of the UK’s cosmopolitan capital city, and students at the university are ideally placed to experience everything that London has to offer. It offers a portfolio of over 250 taught and research postgraduate programmes spanning a range of subject areas, including the humanities and social sciences, business, law, and science. Students at the university are members...more

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