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  • Andrew Hyam BSc MA

    Learn more about our Senior Supervisor of ULAS, Andrew Hyam.

  • Writing for Laughs

    Module code: EN3204 This module aims to mix creative writing, criticism and theory in its exploration of modern and contemporary comedy.

  • Advanced Statistical Modelling (Part-time)

    Module code: MD7468 Generalised linear models This module will start by introducing the theory of generalized linear models (GLMs) in terms of exponential family of distributions and discussing special cases of GLMs, such as normal, Poisson or binomial regression.

  • Molecular Cell Biology and Genomes

    Module code: NT3004 'Molecular Cell Biology’ is centred on the principal techniques used in molecular cell biology and allows you to develop your ability to design experiments to test a hypothesis, based on the use of these techniques.

  • Risk reduction

    How can I reduce my risk of developing a wound infection identifies options of how to stay healthy prior to surgery

  • Research resources

    The University of Leicester holds excellent research resources for medieval studies, such as the significant collection of manuscripts and rare books held in the University Library. Find out more about these available resources.

  • Advanced Statistical Modelling (Part-time)

    Module code: MD7468 Generalised linear models This module will start by introducing the theory of generalized linear models (GLMs) in terms of exponential family of distributions and discussing special cases of GLMs, such as normal, Poisson or binomial regression.

  • Writing for Laughs

    Module code: EN3204 This module aims to mix creative writing, criticism and theory in its exploration of modern and contemporary comedy.

  • Writing for Laughs

    Module code: EN3204 This module aims to mix creative writing, criticism and theory in its exploration of modern and contemporary comedy.

  • Advanced Statistical Modelling (Part-time)

    Module code: MD7468 Generalised linear models This module will start by introducing the theory of generalized linear models (GLMs) in terms of exponential family of distributions and discussing special cases of GLMs, such as normal, Poisson or binomial regression.

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