A PhD at Imperial College London is a demanding academic qualification. We therefore look for evidence of strong and consistent academic performance and expect applicants to have high grades at undergraduate and Master’s level, including a strong dissertation. From graduates of UK institutions, this would normally equate to a combination of at least an Upper Second Class Honours degree at undergraduate level and a Master’s degree awarded with Distinction. Please see the College Country index for the international equivalent of these qualifications, though please bear in mind that the Business School may ask for higher grades than the College minimum.
In exceptional circumstances, we will consider candidates holding a strong First Class Honours undergraduate degree, without a Master’s, if accompanied by very strong academic references and outstanding CV and personal statement.
For general enquiries, please contact us at firstname.lastname@example.org
What to expect – pre-requisite knowledge and training
As a PhD graduate of Imperial College Business School, you will be expected to have a solid foundation in quantitative research methods, understand the breadth of your research area and have in depth knowledge of your specific field demonstrated through your own original research. Graduates who are working in primarily qualitative fields are still expected to undertake core quantitative methods training.
To be able to succeed in the formal courses, as a minimum, you should have knowledge of:
Topics include functions, limits and continuity, differentiation, applications of the derivative, curve sketching, and integration theory, methods of integration, applications of the integral, Taylor’s theorem, infinite sequences and series
Matrix Theory/Linear Algebra
Topics include matrix algebra, systems of linear equations, determinants, vector algebra and geometry, eigenvalues, eigenvectors, vector spaces, subspaces, bases, and dimension, linear transformations, representation by matrices, nullity, rank, diagonalization, inner products, adjoints, unitary, and orthogonal transformations
Topics include fundamentals of probability theory, confidence intervals, and tests of hypothesis for normal distributions, one- and two-sample tests and associated confidence intervals for means and proportions, analysis of variance, F-tests, correlation, regression, contingency tables, and statistical analysis using the computer
Data analysis and programming
During your courses and research, you will use a variety of analysis tools and programming languages, including R, STATA, MATLAB, Python – applicants who are not confident with programming should learn before enrolling. There are many open online courses available that make it feasible to learn. This Data School web page gives a good round up of some available resources as a starting point.
You can download a copy of the first chapter of our Data Analysis Tools course notes and work through the exercises to test your own knowledge.