Data Science MSc

This MSc programme is aimed at students with a background in mathematics and statistics who wish to develop their skills in computing and Information technologies, data synthesis and processing, and also their statistical skills for the analyses of large and complex data structures.

Within the programme, two specialism routes are available, statistical inference and Computing.

Statistical Inference Specialism

A data scientist is a highly skilled individual with the ability to: articulate a research question; gather, process and model data at large-scale (developing scalable algorithms for performing inference and modelling complex and heterogeneous data structures) and to disseminate research findings in context.

This specialism provides a thorough training in data science fundamentals but with added focus upon statistical modelling and inference. Students will gain thorough training in computing, data science technologies, statistical inference, statistical modelling and applied data analysis. The MSc will involve a mixture of taught modules and a research dissertation with a placement in industry or with a research organisation.

Computing Specialism

Underpinning the data scientist role are the technologies that enable the processing of data at large-scale, often using parallel processing paradigms.

This specialism provides the training to understand how these technologies function and how they are implemented within both enterprise and research environments. Students will get hands-on experience of building, from scratch, large-scale systems that enable data science questions to be answered, using technologies such Hadoop, Spark, Giraph, and HBase. This MSc will involve a mixture of taught modules and a research dissertation with a placement in industry or with a research organisation.

Statistical Inference Specialism

The Department of Mathematics and Statistics directs the Statistical Inference Specialism. The course consists of a series of taught modules followed by the completion of a dissertation. The taught course component consists of 9 modules which can be decomposed as follows.

  • A ‘core’ set of five compulsory modules spanning modern data science:
  • A compulsory ‘specialist’ module’:
  • Three self-selected optional’ modules chosen from:

Detailed module descriptions are available in the linked document.

Dissertation Project

The dissertation project involves a substantial piece of individual project work on an aspect of Data Science with dedicated supervision from one of our internationally recognized members of staff.

As part of the dissertation project, you will undertake a period of internship within an organisation to get hands-on experience of the Data Scientist role within the respective organisation and what this entails. This internship will be supported by a placement bursary.

Scholarship information can be found on the Lancaster Data Science web site.

Course Timetabling

The Data Science Course is delivered over three terms. Taught courses are delivered in the first two terms through dedicated through dedicated scheduled sessions combining lectures with interactive workshops. Four of the core modules run in the Michaelmas term (term I); Bayesian Inference and the optional modules run in Lent term (term II) and the Data Science Fundamentals module runs over terms I and II.

Examinations (where applicable) are held during the period May-June. The dissertation period commences immediately thereafter.

University Term Dates 2014-2015

Michaelmas Term:
3 October 2014 to 12 December 2014
Lent Term:
9 January 2015 to 20 March 2015
Summer Term:
17 April 2015 to 26 June 2015
Dissertation period:
June 2015 to September 2015

Key Facts

Unlike other Data Science MSc offerings in the UK, Lancaster University is offering a programme that combines interdisciplinary teaching from three world-leading departments:

Also, as a Data Science student at Lancaster you will have access to: