Masters | Postgraduate Course - Network Science MSc Course Details within Queen Mary University of London

Graduate Admissions Office
Tel:020 7882 5533
Fax:020 7882 5588
Course Fees
Home and EU students 2018/19 Academic Year Full time £9,250 Part time £4,650 International students 2018/19 Academic Year Full time £19,500 Part time £9,750
Start Month(s)
Contact Name
School of Mathematical Sciences
+44 (0)20 7882 5468
Network Science MSc

Study Details

MSc 1 year Full time 2 years Part time

Network Science is a very active and rapidly evolving research field with high societal impact, which stands at the crossroads of graph theory, complexity and data analysis. The goal of network science is to develop tools to analyse Big Data of interacting complex networks and to propose numerical and analytical frameworks to predict their behaviour.

Since in these decades we are witnessing an exponential growth of data concerning communication networks and global infrastructures, the financial system, on-line social networks and biological networks, Network Science stands as a new discipline to cope with some of the most challenging endeavours we face today, in an ever increasingly more connected society.

Its impact and applications outside academia pervade technological sectors, finance, marketing and IT, public health and network biology, to cite a few.

This specialist masters programme aims at providing graduate students and professionals with a rigorous training in the underlying mathematical concepts, the analysis and modelling of complex networks and networked systems, complemented with training in computing, numerical simulations and massive data analysis. It is aimed towards students with a mathematical background who wish to enter a career involving analysis and optimisation of diverse kinds of networks, networked dynamics and models.

Module details

Semester 1 - Compulsory modules

  • Graphs and Networks
  • Research Methods in Mathematical Sciences
  • Topics in Scientific Computing

Semester 1 - Elective modules

  • Data Mining
  • Dynamical Systems
  • Machine Learning

Semester 2 - Compulsory modules

  • Processes on Networks
  • Digital Media and Social Networks
  • Dissertation

Semester 2 - Elective modules

  • Complex Systems
  • Computational Statistics with R
  • Database Systems
  • Trading and Risk Systems Development
  • Machine Learning with Python

Entry Requirements

The majority of our applicants will have an undergraduate degree with first class or upper second class honours (or international equivalent).  Offers will typically be made at 2.1 level (upper second class) or equivalent.  Students with a good lower second class degree may be considered on an individual basis.  In some cases your offer may include additional conditions, such as minimum grades in specified modules, in order to ensure that you are sufficiently qualified for our MSc programmes.

Students applying to this programme should have studied a subject with a substantial mathematical component at the undergraduate level.  We welcome those from a variety of relevant disciplines, including mathematics, statistics, physics, engineering, economics and computer science.

Programme Funding

There are a number of sources of funding available for Masters students.

These include a significant package of competitive Queen Mary University of London (QMUL) bursaries and scholarships in a range of subject areas, as well as external sources of funding.