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Data Visualisation and Modelling (PGCert)

Course Description

This Postgraduate Certificate course in Data Visualisation and Modelling provides graduates with a comprehensive understanding of the mathematical, statistical and data visualisation techniques needed to investigate problems in a wide range of applications.

With recent developments in digital technology, society has entered the era of ‘Big Data’. However, the explosion and wealth of available data gives rise to new challenges and opportunities in all disciplines – from science and engineering to biology and business.

A major focus is on the need to take advantage of an unprecedented volume of data in order to acquire further insights and knowledge.

The flexibility of this course makes it particularly suitable for students in employment.

See the website http://www.brookes.ac.uk/courses/postgraduate/data-visualisation-and-modelling/

Why choose this course?

- A flexible approach to study enables participants to complete the Postgraduate Certificate course in between 1 and 5 years (part-time).

- Use of SPSS.

- A course designed to increase employability in a high-demand field of work.

- Develop your critical skills in the application of visualisation techniques for understanding and presenting the results of analysis.

- Join a supportive and close-knit community of teachers, support staff and learners.

This course in detail

Advanced Statistical Modelling - This module introduces a broad class of linear and non-linear statistical models and the principles of statistical inference to a variety of commonly encountered data analysis problems. The software package SPSS will be used as a tool for statistical analysis with the goal of enabling students to develop their critical thinking and analytical skills. The emphasis, however, is very much on the practical aspect of the methodology and techniques with the theoretical basis kept at a minimum level.

Modelling and Data Analysis using MATLAB - This module gives depth of knowledge in advanced modelling techniques and breadth of analysis by virtue of its general application to any field of engineering and data analysis. In this module students learn to build computer models, present and analyse data using the facilities of MATLAB. Some mathematics is taught as relevant to data interpolation, optimisation and/or choosing solvers for models featuring differential equations.

Data Visualisation and Applications - This module provides a general but broad grounding in the principles of data visualisation and its applications. It covers an introduction to perception and the human visual system, design and evaluation of visualisation techniques, analysing, organising and presenting information visually, using appropriate techniques and visualisation systems.

Teaching and learning

The programme follows a supportive teaching and learning strategy based on active student engagement.

Modules offer a variety of teaching methods, and feature a selection of critical appraisal reports, the use of software applications for data analysis, presentations and case studies.

Learning methods include blended learning, formal lectures and problem solving practicals, but also guided independent learning, use of the virtual learning environment Moodle, independent research, software data analyses, and experiments.

Approach to assessment

Due to the data analysis and the interpretive nature of the course content, the high level industrial participation, and the authentic nature of the assessment, all modules are assed entirely by coursework which includes in-class tests. The assessment regime is selected according to what is appropriate for the material covered.

Attendance pattern

Students will study one twelve-week module per semester, attending campus one day per week for six weeks for each module. A typical module delivery structure is as follows.
- Face to face lectures will take place in weeks 2-5. Each face to face session is three hours, and there will be two face-to-face sessions per day.

- A two-hour class test and individual discussion of mini-projects will take place in week 6.

- An online surgery is available to support guided self-study in weeks 7-11.

- E-learning materials will be available throughout the semester as required on Moodle.

- Weekly exercises for formative feedback will be submitted into a drop box for each module.

- Mini-projects will be due at the end of week 12.


Currently, global demand for combined statistical, mathematics and computing expertise outstrips supply, with evidence-based predictions suggesting a major shortage in this area for at least the next 10 years.

For graduates in data visualisation and modelling this shortage presents opportunities to enhance career progression in one of the most crucial areas of modern science.

Free language courses for students - the Open Module

Free language courses are available to full-time undergraduate and postgraduate students on many of our courses, and can be taken as a credit on some courses.

Please note that the free language courses are not available if you are:
- studying at a Brookes partner college
- studying on any of our teacher education courses or postgraduate education courses.

Visit the Data Visualisation and Modelling (PGCert) page on the Oxford Brookes University website for more details!

Entry Requirements

A good (first or second class) STEM based degree which has developed analytical knowledge and understanding in mathematical sciences. Typically this includes candidates with knowledge and familiarity with basic mathematics and statistics concepts and methods at a degree level. Applicants with other qualifications plus work experience from other fields who have quantitative skills and familiarity with data analysis and modelling ideas, to be reflected in their application, will also be considered.

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