From business and finance to health and medicine, from infrastructure to societal studies, data science plays a vital role in all aspects of the modern world. The programme provides a foundation in statistical modelling and data analytical skills; the theories underpinning statistical modelling; programming; data mining and data science as process for gaining insight from data. You can study either full-time or part-time.
Upon successful completion of the modules you may wish to progress to a PGDip or an MSc in Data Science.
2:1 Hons degree (UK or equivalent) in Statistics, Computer Science or similar.
We may also consider non-standard applicants, please contact us for information.
If you have studied outside of the UK, we would advise you to check our list of international qualifications before submitting your application.
English Language Requirements
We may ask you to provide a recognised English language qualification, dependent upon your nationality and where you have studied previously.
We normally require an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 6.0 in each element of the test. We also consider other English language qualifications.
If your score is below our requirements, you may be eligible for one of our pre-sessional English language programmes.
Contact: Admissions Team +44 (0) 1524 592032 or email email@example.com
You will study a range of modules as part of your course, some examples of which are listed below.
Students are provided with a comprehensive coverage of the problems related to data representation, manipulation and processing in terms of extracting information from data, including big data. They will apply their working understanding to the data primer, data processing and classification. They will also enhance their familiarity with dynamic data space partitioning, using evolving, clustering and data clouds, and monitoring the quality of the self-learning system online.
Students will also gain the ability to develop software scripts that implement advanced data representation and processing, and demonstrate their impact on performance. In addition, they will develop a working knowledge in listing, explaining and generalising the trade-offs of performance, as well as the complexity in designing practical solutions for problems of data representation and processing in terms of storage, time and computing power.
Data Science Fundamentals
This module will help you understand what the data science role entails and how that individual performs their job within an organisation on a day-to-day basis. You will look at how research is performed in terms of formulating a hypothesis and the implication of research findings, and be aware of different research strategies and when these should be applied. You will gain an understanding of data processing, preparation and integration, and how this enables research to be performed and you will learn how data science problems are tackled in an industrial setting, and how such findings are communicated to people within the organisation.
Programming for Data Scientists
This module is designed for students that are completely new to programming, and for experienced programmers, bringing them both to a high-skilled level to handle complex data science problems. Beginner students will learn the fundamentals of programming, while experienced students will have the opportunity to sharpen and further develop their programming skills. The students are going to learn data-processing techniques, including visualisation and statistical data analysis. For a broad formation, in order to handle the most complex data science tasks, we will also cover problem solving, and the development of graphical applications. Two open source programming languages will be used, R and Python.
Statistical Foundations I
This module will motivate the use of statistical modelling as a tool for making inference on a population given a sample of data. Students will be introduced to basic terminology of statistical modelling, and the similarities and differences between statistical and machine learning approaches will be discussed to lay the foundations for the development of both of these over the remaining core modules They will cover the concepts of sampling uncertainty, statistical inference and model fitting, with sampling uncertainty used to motivate the need for standard errors and confidence intervals. Once core concepts have been established, linear regression and generalised linear models will be introduced as essential statistical modelling tools. An understanding of these models will be obtained through implementation in the statistical software package R.
Statistical Fundamentals I
This module provides an introduction, at graduate level, to two core areas which are essential building blocks to further advanced study of statistical modelling, methodology and theory. The areas that will be covered are statistical inference using maximum likelihood and generalised linear models (GLMs). Building on an undergraduate level understanding of mathematics, statistics (hypothesis testing and linear regression) and probability (univariate discrete and continuous distributions; expectations, variances and covariances; the multivariate normal distribution), this module will motivate the need for a generic method for model fitting and then demonstrate how maximum likelihood provides a solution to this. Following on from this, GLMs, a widely and routinely used family of statistical models, will be introduced as an extension of the linear regression model.
Applied Data Mining
This module provides students with up-to-date information on current applications of data in both industry and research. Expanding on the module ‘Fundamentals of Data’, students will gain a more detailed level of understanding about how data is processed and applied on a large scale across a variety of different areas.
Students will develop knowledge in different areas of science and will recognise their relation to big data, in addition to understanding how large-scale challenges are being addressed with current state-of-the-art techniques. The module will provide recommendations on the Social Web and their roots in social network theory and analysis, in addition their adaption and extension to large-scale problems, by focusing on primer, user-generated content and crowd-sourced data, social networks (theories, analysis), recommendation (collaborative filtering, content recommendation challenges, and friend recommendation/link prediction).
On completion of this module, students will be able to create scalable solutions to problems involving data from the semantic, social and scientific web, in addition to abilities gained in processing networks and performing of network analysis in order to identify key factors in information flow.
Building Big Data Systems
In this module we explore the architectural approaches, techniques and technologies that underpin today's Big Data system infrastructure and particularly large-scale enterprise systems. It is one of two complementary modules that comprise the Systems stream of the Computer Science MSc, which together provide a broad knowledge and context of systems architecture enabling students to assess new systems technologies, to know where technologies fit in the larger scheme of enterprise systems and state of the art research thinking, and to know what to read to go deeper.
The principal ethos of the module is to focus on the principles of Big Data systems, and applying those principles using state of the art technology to engineer and lead data science projects. Detailed case studies and invited industrial speakers will be used to provide supporting real-world context and a basis for interactive seminar discussions.
Clinical trials are planned experiments on human beings designed to assess the relative benefits of one or more forms of treatment. For instance, we might be interested in studying whether aspirin reduces the incidence of pregnancy-induced hypertension, or we may wish to assess whether a new immunosuppressive drug improves the survival rate of transplant recipients.
This module combines the study of technical methodology with discussion of more general research issues, beginning with a discussion of the relative advantages and disadvantages of different types of medical studies. The module will provide a definition and estimation of treatment effects. Furthermore, cross-over trials, issues of sample size determination, and equivalence trials are covered. There is an introduction to flexible trial designs that allow a sample size re-estimation during the ongoing trial. Finally, other relevant topics such as meta-analysis and accommodating confounding at the design stage are briefly discussed.
Students will gain knowledge of the basic elements of clinical trials. They will develop the ability to recognise and use principles of good study design, and will also be able to analyse and interpret study results to make correct scientific inferences.
Distributed Artificial Intelligence
Distributed artificial intelligence is fundamental in contemporary data analysis. Large volumes of data and computation call for multiple computers in problem solving. Being able to understand and use those resources efficiently is an important skill for a data scientist. A distributed approach is also important for fault tolerance and robustness, as the loss of a single component must not significantly compromise the whole system. Additionally, contemporary and future distributed systems go beyond computer clusters and networks. Distributed systems are often comprised of multiple agents -- multiple software, humans and/or robots that all interact in problem solving. As a data scientist, we may have control of the full distributed system, or we may have control of only one piece, and we have to decide how it must behave in face of others in order to accomplish our goals.
Every managerial decision concerned with future actions is based upon a prediction of some aspects of the future. Therefore Forecasting plays an important role in enhancing managerial decision making.
After introducing the topic of forecasting in organisations, time series patterns and simple forecasting methods (naïve and moving averages) are explored. Then, the extrapolative forecasting methods of exponential smoothing and ARIMA models are considered. A detailed treatment of causal modelling follows, with a full evaluation of the estimated models. Forecasting applications in operations and marketing are then discussed. The module ends with an examination of judgmental forecasting and how forecasting can best be improved in an organisational context. Assessment is through a report aimed at extending and evaluating student learning in causal modelling and time series analysis.
Methods for Missing Data
Almost every set of data, whether it consists of field observations, data from laboratory experiments, clinical trial outcomes, or information from population surveys or longitudinal studies, has an element of missing data. For example, participants in a survey or clinical trial may drop-out of the study, measurement instruments may fail, or human error invalidate instrumental readings. Missingness may or may not be related to the information being collected; for instance, drop out may occur because a patient dislikes the side-effects of an experimental treatment or because they move out of the area or because they find that they no longer have the time to attend follow up appointments. In this module you will learn about the different ways in which missing data can arise, and how these can be handled to mitigate the impact of the missingness on the data analysis. Topics covered include single imputation methods, Bayesian imputation, multiple imputation (Rubin's rules, chained equations and multivariate methods, as well as suitable diagnostics) and modelling dropout in longitudinal modelling.
Optimisation and Heuristics
Optimisation, sometimes called mathematical programming, has applications in many fields, including operational research, computer science, statistics, finance, engineering and the physical sciences. Commercial optimisation software is now capable of solving many industrial-scale problems to proven optimality.
The module is designed to enable students to apply optimisation techniques to business problems. Building on the introduction to optimisation in the first term, students will be introduced to different problem formulations and algorithmic methods to guide decision making in business and other organisations.
Principles of Epidemiology
Introducing epidemiology, the study of the distribution and determents of disease in human population, this module presents its main principles and statistical methods. The module addresses the fundamental measures of disease, such as incidence, prevalence, risk and rates, including indices of morbidity and mortality.
Students will also develop awareness in epidemiologic study design, such as ecological studies, surveys, and cohort and case-control studies, in addition to diagnostic test studies. Epidemiological concepts will be addressed, such as bias and confounding, matching and stratification, and the module will also address calculation of rates, standardisation and adjustment, as well as issues in screening.
This module provides students with a historical and general overview of epidemiology and related strategies for study design, and should enable students to conduct appropriate methods of analysis for rates and risk of disease. Students will develop skills in critical appraisal of the literature and, in completing this module, will have developed an appreciation for epidemiology and an ability to describe the key statistical issues in the design of ecological studies, surveys, case-control studies, cohort studies and RCT, whilst recognising their advantages and disadvantages.
Survival and Event History Analysis
This module addresses a range of topics relating to survival data; censoring, hazard functions, Kaplan-Meier plots, parametric models and likelihood construction will be discussed in detail. Students will engage with the Cox proportional hazard model, partial likelihood, Nelson-Aalen estimation and survival time prediction and will also focus on counting processes, diagnostic methods, and frailty models and effects.
The module provides an understanding of the unique features and statistical challenges surrounding the analysis of survival avant history data, in addition to an understanding of how non-parametric methods can aid in the identification of modelling strategies for time-to-event data, and recognition of the range and scope of survival techniques that can be implemented within standard statistical software.
General skills will be developed, including the ability to express scientific problems in a mathematical language, improvement of scientific writing skills, and an enhanced range of computing skills related to the manipulation on analysis of data.
On successful completion of this module, students will be able to apply a range of appropriate statistical techniques to survival and event history data using statistical software, to accurately interpret the output of statistical analyses using survival models, fitted using standard software, and the ability to construct and manipulate likelihood functions from parametric models for censored data. Students will also gain observation skills, such as the ability to identify when particular models are appropriate, through the application of diagnostic checks and model building strategies.
Information contained on the website with respect to modules is correct at the time of publication, but changes may be necessary, for example as a result of student feedback, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes, and new research. Not all optional modules are available every year.
Fees and Funding
|Location||Full Time (per year)||Part Time (per year)|
Scholarships and bursaries
At Lancaster, we believe that funding concerns should not stop any student with the talent to thrive.
We offer a range of scholarships and bursaries to help cover the cost of tuition fees and/or living expenses.
There may be extra costs related to your course for items such as books, stationery, printing, photocopying, binding and general subsistence on trips and visits. Following graduation, you may need to pay a subscription to a professional body for some chosen careers.
Specific additional costs for studying at Lancaster are listed below.
Lancaster is proud to be one of only a handful of UK universities to have a collegiate system. Every student belongs to a college, and all students pay a small which supports the running of college events and activities.
For students starting in 2022, the fee is £40 for undergraduates and research students and £15 for students on one-year courses. Fees for students starting in 2023 have not yet been set.
Computer equipment and internet access
To support your studies, you will also require access to a computer, along with reliable internet access. You will be able to access a range of software and services from a Windows, Mac, Chromebook or Linux device. For certain degree programmes, you may need a specific device, or we may provide you with a laptop and appropriate software - details of which will be available on relevant programme pages. A dedicated IT support helpdesk is available in the event of any problems.
The University provides limited financial support to assist students who do not have the required IT equipment or broadband support in place.
Fees in subsequent years
The University will not increase the Tuition Fee you are charged during the course of an academic year.
If you are studying on a programme of more than one year's duration, the tuition fees for subsequent years of your programme are likely to increase each year. The way in which continuing students' fee rates are determined varies according to an individual's 'fee status' as set out on our fees webpages.
Data, Computing and Communications
Mathematics and Statistics
- Data Science MSc
- Data Science PgDip
- Mathematics PhD
- Natural Sciences MSc by Research
- Natural Sciences PhD
- Social Statistics PhD
- Statistics MSc
- Statistics PGDip
- Statistics PhD
- Statistics PhD (Integrated)
- Statistics and Epidemiology PhD
- Statistics and Operational Research MRes
- Statistics and Operational Research (STOR-i) PhD
We offer an excellent range of learning environments, which include traditional lectures, computer laboratories, and workshops. We are also committed to providing timely feedback for all submitted work and projects.
Assessment varies across modules, allowing students to demonstrate their capabilities in a range of ways. Assessment can include laboratory reports, essays, exercises, literature reviews, short tests, poster sessions, oral presentations and formal examinations.
We have an excellent relationship with our students and alumni. We have received praise for our ambition, positivity and supportiveness. By providing a variety of support methods, accessible at all stages of your degree, we strive to give our students the best opportunity to fulfil their potential and attract the very best opportunities for a successful career. Our academics are welcoming and helpful. We will assign an academic advisor to you who can offer advice and recommended reading. Our open-door policy has been a popular feature among our students. We believe in encouraging and inspiring our data scientists of the future.
Your future employability
The demand for people with data science skills is predicted to double over the next five years. This rising need is reflected in the average salary for data scientists, which is now £60,000 per annum.
Scholarships and funding
If you have graduated with a Bachelor or Master's degree from Lancaster, we offer a 10% or 20% reduction in tuition fees to all taught Master's degrees, depending on the results of the degree you graduate with.
Lancaster University will offer a scholarship of £5000 to high-achieving students who are liable for International tuition fees.
Lancaster University will offer a £4000 scholarship to high-achieving Master's students who are liable for Home tuition fees.
The UK Government offers postgraduate loans of up to £11,836 to Master's level students. A Postgraduate Master's Loan can help with course fees and living costs while you study a postgraduate master's course.
The information on this site relates primarily to 2022/2023 entry to the University and every effort has been taken to ensure the information is correct at the time of publication.
The University will use all reasonable effort to deliver the courses as described, but the University reserves the right to make changes to advertised courses. In exceptional circumstances that are beyond the University’s reasonable control (Force Majeure Events), we may need to amend the programmes and provision advertised. In this event, the University will take reasonable steps to minimise the disruption to your studies. If a course is withdrawn or if there are any fundamental changes to your course, we will give you reasonable notice and you will be entitled to request that you are considered for an alternative course or withdraw your application. You are advised to revisit our website for up-to-date course information before you submit your application.
More information on limits to the University’s liability can be found in our legal information.
Our Students’ Charter
We believe in the importance of a strong and productive partnership between our students and staff. In order to ensure your time at Lancaster is a positive experience we have worked with the Students’ Union to articulate this relationship and the standards to which the University and its students aspire. View our Charter and other policies.