Often known as the science of uncertainty, statistics is of vital importance in modern society where almost all sectors rely on the collection, analysis and interpretation of data. There is a great shortage of well qualified statisticians, data analysts and statistical consultants across the sector and this course has been specifically designed to meet that demand.
Applied statistics involves putting theory into practice - not only summarising and describing data, but extrapolating from it to draw conclusions about the population being studied. This is an applied, practically-orientated course that gives you advanced knowledge of statistical methods and the theory that underpins these methods. With a strong emphasis on relating theory to practice, you will develop your analytical, logical, numerical and problem-solving, skills that are in such high demand with employers. You'll also learn how to use standard statistical software like R, SPSS and Minitab.
You'll have the freedom to choose the type of independent research project you do which can take the form of a theoretical dissertation, a survey or a more practical project involving a data set. If you're working, you'll have the option of basing your project at your workplace – making your studies even more relevant and beneficial for both you and your employer.
*Please note this course is subject to review.
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You’ll gain a thorough understanding of mathematical and statistical concepts and techniques and how to apply them to data sets. You’ll develop an advanced knowledge of data collection methods, the statistical process, exploratory data analysis, statistical modelling, probability, statistical inference and methods of analysis, and will work on applied problems. You’ll learn how to obtain different types of data from a variety of sources, including electronic databases; analyse it using programming and computer packages; and compare and choose between different methods of modelling and analysis. The course also covers big data, and the use of both small samples and big data to make judgments about large populations.
The quantity of data available to analysts is growing at an ever-increasing rate. This data has become a vital tool for decision-making in a competitive world. However, the size, which makes the data so valuable, also makes it difficult to analyse using traditional statistical methods. This module introduces the student to a variety of methodologies now employed to explore, analyse, categorise and visualise data from large data sets.
On this module, students are taught the important concepts of descriptive statistical analysis applied on different types of data sets. The course will develop students’ appreciation of the task of a statistician for critically analysing data sets and will be useful to anyone considering a job in statistics. Students will develop a keener understanding about structures that underlie data observations.
This module aims to give students a solid grounding in some of the most important analysis methods. It looks at the different practices and assumptions made in different applied scientific disciplines. It provides students with an understanding of the empirical techniques commonly used in statistical analysis as well as the ability to use these techniques and critically evaluate and interpret empirical work.
This module aims to introduce students to advanced techniques in inference theory. It develops students’ ability to understand statistical theory as well as applying it to computational methods. Students are introduced to a wide-range of advanced techniques in classical inference and are given a practical introduction to Bayesian analysis.
The project allows students to consolidate their learning in a substantial piece of independent work utilising the skills and knowledge developed in the taught content. Students have the opportunity in this module to study a problem that interests them and that requires further study, allowing students to demonstrate expertise in problem definition, research design, analysis and critical presentation of the results.
Data obtained from observations collected sequentially over time are extremely common. The purpose of time series analysis is to understand or model the stochastic mechanism that gives rise to an observed series and to predict or forecast the future values of a series based on the history of that series, and, possibly, other related series or factors.
This module aims to give students a solid grounding in some of the most important methods employed by statisticians by providing a deeper understanding of probability theory and random processes. Students will be introduced to modern topics and techniques in stochastic processes. They will learn the relevant theory and gain the ability to formulate and solve practical problems.
You can find more information about this course in the programme specification. Module and programme information is indicative and may be subject to change.
We are regularly reviewing and updating our programmes to ensure you have the best learning experience. We are taking what we've learnt during the pandemic and enhancing our teaching methods with new and innovative ways of learning. Please regularly check this section of the course page for updates.
Lectures and talks will introduce you to concepts and techniques, which you’ll explore further through workshops, seminars, and discussions in class. Examples will help you to relate theory to practice, and we’ll encourage you to think critically. You’ll supplement all this with your own independent reading and study, including the use of online resources.
You’ll be assessed through coursework, tests and your project.
We have developed new approaches to teaching and learning since the 2021/22 academic year, and have resumed the majority of our teaching on campus.
We are currently reviewing our approach to teaching and learning for 2023 entry and beyond. We've learned a lot about how to give you a quality education - we aim to combine the best of our pre-pandemic teaching and learning with access to online learning and digital resources which put you more in charge of when and how you study. We will keep you updated on this throughout the application process.
Your timetable will be built around on campus sessions using our professional facilities, with online sessions for some activities where we know being virtual will add value. We’ll use technology to enhance all of your learning and give you access to online resources to use in your own time.
The table below gives you an idea of what learning looks like across a typical week. Some weeks are different due to how we schedule classes and arrange on campus sessions.
This information is likely to change slightly for 2023 entry as our plans evolve. You'll receive full information on your teaching before you start your course.
Learning structure: typical hourly breakdown in 2022/23 | ||
Live in-person on campus learning | Contact hours per week, per level: | 9 hours |
Live online learning | Average hours per week, per level: | 3 hours |
Outside of these hours, you’ll be expected to do independent study where you read, listen and reflect on other learning activities. This can include preparation for future classes. In a year, you’ll typically be expected to commit 1200 hours to your course across all styles of learning. If you are taking a placement, you might have some additional hours.
Definitions of terms
Support
You have a strong support network available to you to make sure you develop all the necessary academic skills you need to do well on your course.
Our support services will be delivered online and on campus and you have access to a range of different resources so you can get the help you need, whether you’re studying at home or have the opportunity to come to campus.
You have access to one to one and group sessions for personal learning and academic support from our library and IT teams, and our network of learning experts. Our teams will also be here to offer financial advice, and personal wellbeing, mental health and disability support.
There is a need in both the public and private sectors for well-qualified statisticians and this course will leave you ideally placed for a wide variety of employment opportunities in:
You might also wish to explore your options in research or academia, or even complete further study at doctoral level.
Statisticians work in many fields, from government to market research, measuring anything from changes in the environment revealing the effects of global warming to the effectiveness of medicines. There are a large number of employment opportunities for our graduates in medical statistics, medical research, commerce and industry, particularly the pharmaceutical industry. There are also many career opportunities both in areas directly related to statistics, such as economics and accountancy, and wider field in areas like health sciences, natural and environmental sciences, computing, engineering and law.
We’ll carefully manage any future changes to courses, or the support and other services available to you, if these are necessary because of things like changes to government health and safety advice, or any changes to the law.
Any decisions will be taken in line with both external advice and the University’s Regulations which include information on this.
Our priority will always be to maintain academic standards and quality so that your learning outcomes are not affected by any adjustments that we may have to make.
At all times we’ll aim to keep you well informed of how we may need to respond to changing circumstances, and about support that we’ll provide to you.
Start: September 2023, EU/INT induction: September 2023
Duration: 1 year full-time, 2 years part-time
Code: PGG1N3
