Why this course?

Our MSc in Applied Statistics is a conversion course, offering the opportunity to develop skills in statistics and data analysis even if you have never studies statistics before. You will be supported by members of staff who work directly with industry to develop skills which are relevant to current areas of research including population health and medicine, animal and plant health, finance and business.

Students will gain skills in:

• problem solving
• analysis and manipulation of complex data
• the use of statistical software for data analysis and reporting
• effective communication of statistics

Programme skillset

On the MSc Applied Statistics programme you'll have the opportunity to acquire:

• an in-depth knowledge of modern statistical methods used to analyse and visualise real-life data sets, and the experience of how to apply these methods in a professional setting
• skills in using statistical software packages used in government, industry and commerce
• the ability to interpret the output from statistical tests and data analyses, and communicate your findings to a variety of audiences including health professionals, scientists, government officials, managers and stakeholders who may have an interest in the problem
• problem solving and high numeracy skills widely sought after in the commercial sector
• practical experience of statistical consultancy and how to interact with professionals who require statistical analyses of their data

What you’ll study

In addition to compulsory modules, there are a range of elective modules, meaning you can tailor the course in line with your career interests.

Semester 1
In semester 1, modules focus on the foundations of statistics. You’ll learn about probability, and basic statistical analysis, as well as developing skills in programming in the statistical programming language R.

Semester 2
In semester 2, modules will build on concepts from year 1. These will focus on methods of analysis that can be applied to specific areas, such as medical trials, risk analysis, and finance.

Semester 3
In semester 3, you'll undertake a research project in which you'll work on a real-life data set, putting the theoretical skills you have learned into practice.

Learning & teaching

Classes are delivered by a number of teaching methods:

• lectures (using a variety of media including electronic presentations and computer demonstrations)
• tutorials
• computer laboratories
• coursework
• projects

Teaching is student-focused, with students encouraged to take responsibility for their own learning and development. Classes are supported by web-based materials.

Assessment

The form of assessment varies from class to class. For most classes the assessment involves both coursework and examinations.

• The assessment will take ask you to demonstrate your statistical knowledge and skills to analyse real world data and interpret the results in the context of the research question
• Projects will involve writing code, interpreting statistical outputs, and producing a report, or presentation outlining the findings from your analysis
• Group work may be undertaken in some classes

Facilities

The Department of Mathematics & Statistics has teaching rooms which provide you with access to modern teaching equipment and University computing laboratories, with all necessary software available.

You'll also have access to a common room facility which gives you a modern and flexible area for individual and group study work and is also a relaxing social space.

The Department of Mathematics & Statistics

At the heart of the Department of Mathematics & Statistics is the University’s aim of developing useful learning. We're an applied department with many links to industry and government. Most of the academic staff teaching on this course hold joint-appointments with, or are funded by, other organisations, including APHA, Public Health and Intelligence (Health Protection Scotland), NHS Greater Glasgow and Clyde and the Marine Alliance for Science and Technology Scotland (MASTS). We bridge the gap between academia and real-life. Our research has societal impact.