MSc Applied Data Science and Statistics
|Duration||Full time 1 year|
MSc Applied Data Science and Statistics is a full-time Masters course for those wanting to work with and derive meaning from data.
You will learn how work with data and to perform statistical analysis to answer questions.
You will also learn how to interpret and communicate results in the presence of bias and uncertainty.
A variety of languages essential to Data Science will be taught, including R and Python. However, prior experience of programming is not necessary.
As a graduate of this programme you will be capable of extracting otherwise-hidden information within data to use it to make informed decisions.
The programme will include a wide variety of applications and is relevant to those looking for careers working with data in a wide variety of sectors.
Working with Data
The ability to extract information from data as a basis for evidence-based decision is becoming increasing important across a wide variety of sectors in the world of big data, including industry, finance, health, and the environment. This module will equip you with the tools required to collate, import and manipulate data together with methods for basic inference. You will be introduced to different types and sources of data and the tools for performing initial data analysis including producing simple graphical summaries of data and more sophisticated methods for visualising structures in data. These techniques are crucial both as the basis for communication and informing more complex modelling.
Introduction to Data Science and Statistical Modelling
In this module you will learn the basics of statistical inference, including probability, sampling variability, hypothesis testing and how to identify patterns in data and to represent them using statistical models. You will learn the essential mathematical techniques that are required for the implementation and interpretation and statistical and machine learning methods. You will learn how to fit statistical models to data, to evaluate whether models are appropriate given the context of the data and how they can be used to quantify relationships and for prediction.
Applications of Data Science and Statistics
This module will enable you to learn new Data Science and Statistical methods, and to use the techniques learnt in other modules, by working on analyses of real data examples. There will be a strong emphasis throughout on understanding the practical application of statistical and machine learning methods including clustering, data reduction, methods for handling missing data, study design and introductory methods for time series data. Theory and ideas to will be developed to allow the implementation of methods in examples drawn from industry, medicine, finance, public health and environmental challenges, including climate change and air pollution.
Engaging with Research
Critical to every successful academic and industrial career is the ability to keep abreast of developments in your area of expertise. Through engaging with suitable seminars in your specialism, reading research papers and presenting on new developments, you will develop the skills that are essential in a fast-paced and constantly developing environment. You will also be introduced to, and become part of, a thriving research community and will develop key relationships and networking opportunities.
Advanced Topics in Statistics
This module offers an insight to cutting-edge statistical techniques that are at the forefront of current research and application. You will have opportunity to explore a range of topics from time series modelling and forecasting, geostatistics, modelling of extreme values, hierarchical modelling, data fusion, multivariate analysis, computational statistics, and data mining methods, reliability theory and survival analysis, sample survey and experimental design. The choice of topics in any year may change to ensure that the content of the module reflects the rapid change in this exciting area.
Advanced Statistical Modelling
Statistical modelling lies at the heart of modern data analysis. In this module you will learn how the ideas behind linear regression can be developed into a much broader set of models that can be used when dealing with different types of data and for modelling complex relationships between variables. You will be introduced to the Generalized Linear Model (GLM) and extensions including random effects models, Generalized Linear Mixed Models and Generalized Additive Models. You will gain an understanding of both the theoretical basis of advanced statistical models and their application, with examples of the implementation given through the use real-life examples in practical sessions.
Statistical Modelling in Space and Time
Statistical modelling techniques often treat data as independent and identically distributed, but real-world data is often more complex. Data collected in close proximity, in both space and time, are often highly correlated and methods are required that can acknowledge, and exploit, such dependencies within data. In this module, you will learn how to identify and characterise correlations over space and time and gain a thorough understanding of spatial statistics and methods for modelling time series data.
Data Governance and Ethics
Data science, machine learning, artificial intelligence and ‘big data’ have become central to every aspect of modern life. In this module you will examine how these complex and powerful technologies can best be managed and governed for the benefit of society, both now and in the future. You will identify the ethical/legal challenges that are associated with the widespread automation and digitalisation of services, including potential trade-offs between the desire for individual privacy and the institutional commodification of personal data. You will learn how such concerns can be handled and explore the responsibilities of data scientists, and other producers of technologies for data analysis, to ensuring their proper use.
Data Science and Statistics Project
The modules we outline here provide examples of what you can expect to learn on this degree course based on recent academic teaching. The precise modules available to you in future years may vary depending on staff availability and research interests, new topics of study, timetabling and student demand.
Learning and teaching
Aimed at non-specialists wanting to propel themselves in to a career in Data Science, our MSc Applied Data Science and Statistics programme will teach you the skills needed to enter a Data Scientist or Analyst role.
You will be taught to code in R and will also gain experience of Python.
As a graduate of this programme, you will understand how to easily handle extremely large sets of data, and how to extract otherwise-hidden information. You will also learn how to communicate and present your findings.
The programme will include applications across a wide variety of sectors and help you develop innovative and responsible approaches to the use of data. You will cover the entire spectrum from collection through to interrogation and analysis, interpretation, visualisation, and communication.
Employer demand for statistically-trained data scientists is high. A staggering 98% of our 2016/17 computing graduates, and 84% of mathematics graduates, went into work or further study within six months of graduation.
A World Economic Forum report ‘The Future of Jobs’ states that data analysts are likely to be one of two job fields which will be critically important across all industries and geographies by 2020. ‘Companies expect that [data analysts] will help them make sense and derive insights from the torrent of data generated by technological disruptions’.
Whether you’re looking to take your career in a new direction or for an MSc that will sit alongside your undergraduate degree to land you an exhilarating graduate job, you’re unlikely to find a better choice than Applied Data Science and Statistics.
A good degree (normally a 2:2).
Successful applicants will usually have at least an A-level or equivalent in Mathematics and/or have received quantitative skills training as part of their undergraduate programme or professional experience.
Prior experience of coding is not necessary on this course.
Requirements for international students
If you are an international student, please visit our international equivalency pages to enable you to see if your existing academic qualifications meet our entry requirements.
English language requirements
Overall score 6.5. No less than 6.0 in any section.
Overall score 90 with minimum scores of 21 for writing, 21 for listening, 22 for reading and 23 for speaking.
Pearson Test of English (Academic)
58 with no less than 55 in all communicative skills.
Cambridge English: Advanced & Proficiency
Overall score 176. No less than 169 in any section.
Applicants with lower English language test scores may be able to take pre-sessional English at INTO University of Exeter prior to commencing their programme. See our English language requirements page for more information.
Fees and funding
Tuition fees per year 2019/20
- UK/EU: £10,000 full-time
- International: £20,500 full-time
Fees can normally be paid by two termly instalments and may be paid online. You will also be required to pay a tuition fee deposit to secure your offer of a place, unless you qualify for exemption. For further information about paying fees see our Student Fees pages.
UK government postgraduate loan scheme
Postgraduate loans of up to £10,609 are now available for Masters degrees. Find out more about eligibility and how to apply.
Global Excellence Scholarship
We are delighted to offer Global Excellence Scholarships for students of outstanding academic quality applying to postgraduate Taught programmes starting in autumn 2019.
We welcome enquiries about the course.
For further information contact:
Web: Enquire online
Phone: +44 (0) 1392 724061