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  • Study Type

    Full time available

  • Subject Areas

    Computer Science

  • Start Date

    September

  • Course Duration

    1 year full time

  • Course Type

    MSc

  • Course Fees

    website

  • Last Updated

    26 October 2018

Course content

Data is becoming an ever increasing part of modern life, yet the talent to extract information and value from complex data is scarce. This Masters will provide you with a thorough grounding in state-of-the art methods for learning from data, both in terms of statistical modelling and computation. You will also gain practical hands-on experience in carrying out various data-driven analytical projects. Previous study of Statistics or Computing Science is not required.

Why this programme

  • Statistical analysis and data mining is ranked 2nd in the Top 10 Most In-Demand Hard Skills 2018, Linkedin.
  • We work closely with The Data Lab, an internationally leading research and innovation centre in data science. Established with an £11.3 million grant from the Scottish Funding Council, The Data Lab will enable industry, public sector and world-class university researchers to innovate and develop new data science capabilities in a collaborative environment. Its core mission is to generate significant economic, social and scientific value from data. Our students will benefit from a wide range of learning and networking events that connect leading organisations seeking business analytics skills with students looking for exciting opportunities in this field.
  • This programme qualifies for the prestigious Data Lab Masters Scholarships available for Scottish/EU students.
  • The Statistics Group at Glasgow is a large group, internationally renowned for its research excellence.
  • Our expertise spans topics including: biostatistics and statistical genetics; environmental statistics; statistical methodology; statistical modelling and the scholarship of learning and teaching in statistics.

Programme structure

Core courses

  • Bayesian Statistics
  • Big Data Analytics
  • Data Management and Analytics using SAS
  • Generalised Linear Models
  • Introduction to R Programming
  • Preliminary Mathematics for Statisticians 1
  • Probability 2
  • Regression Models2
  • Statistical Inference2

One Course is optional for students with sufficient background in Linear Algebra and Calculus.

Two students who have already completed an equivalent course can substitute this course by any other optional course, including optional courses offered as part of the MRes in Advanced Statistics (see the website for details).

In your project (60 credits) you will model data collected from research in environmental science, assessed by a dissertation.

Optional courses

Students choose at least two courses from group 1 and at least one course from group 2.

Group 1

  • Artificial Intelligence
  • Information Retrieval
  • Machine Learning
  • Programming

Group 2

  • Data Analysis
  • Professional Skills

Group 3

  • Biostatistics
  • Design of Experiments
  • Environmental Statistics
  • Financial Statistics
  • Functional Data Analysis
  • Multivariate Methods
  • Spatial Statistics
  • Stochastic Processes
  • Statistical Genetics
  • Time Series

 In your project (60 credits) you will tackle a complex data analytical problem or develop novel approaches to solving data analytical challenges. 

Programme alteration or discontinuation

The University of Glasgow endeavours to run all programmes as advertised. In exceptional circumstances, however, the University may withdraw or alter a programme. For more information, please see: Student contract.

Career prospects

Statistical analysis and data mining is ranked 2nd in the Top 10 Most In-Demand Hard Skills 2018 by LinkedIn. Our graduates have an excellent track record of gaining employment in many sectors including medical research, the pharmaceutical industry, finance and government statistical services, while others have continued to a PhD.


Visit the Data Analytics (MSc) page on the University of Glasgow website for more details!

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