Student teaching

I teach undergraduate and graduate modules in statistics. A list of modules I'm teaching this academic year is given below.

I also supervise undergraduate- and taught graduate-level final year research projects. If you are a Maths and Stats student on our BSc, MMath or MSc programmes, then lists of suggested project titles will be circulated to you. I'll happily consider supervising a project of your suggestion — drop me a line if you have an idea you wish to discuss. In addition, I'm happy to supervise summer undergraduate research projects by well qualified students — again, contact me about this.

Lastly, I am one of the Adviser of Studies for subhonours students intending to read Mathematics and/or Statistics as their main degree subject. If I'm your Adviser then please feel free to come see me anytime, although it's best to make an appointment first, by emailing me.

Semester 1 (Martinmas) modules

Semester Dates: 12 Sept - 16 Dec 2016

  • MT4531 Bayesian Inference and MT5831 Advanced Bayesian Inference.
    These modules aim to give an introduction to the theory and practice of Bayesian statistical inference. Bayesian statistics is a very attractive alternative approach to the classical methods you have learnt in other modules, offering a rational way to include prior information about a problem and a practical means of making inference for complex problems. MT4531 is the basic course. MT5831 is the same as MT4531 (i.e., same lectures, tutorials and exam) except that there is an additional piece of continuous assessment in the form of a project or essay covering a more advanced topic. I provide a list of topics to choose from, or you can suggest your own.
    • For more information, see the School web site entry for MT4531 and MT5831.
    • If you're signed up, all module materials (course notes, additional readings, tutorials, past exam papers, etc.) are available on the University Module Management System (MMS).
  • MT4113 Computing in Statistics (co-taught with Dr. Eric Rexstad).
    The goal of this module is to teach good programming practice and computing techniques useful to statisticians. The module gives students general programming skills that should be useful in any future computing tasks, and also specific skills using the statistical language and environment R. Assessment is through a series of programming assignments and two in-term class tests; there is no final exam.
    • For more information, see the School web site entry for MT4113.

Semester 2 (Candlemas) modules

Semester Dates: 23 Jan - 26 May 2016

  • No teaching this semester

Training workshops

Our research group, CREEM, organizes training workshops on various aspects of statistics and statistical ecology, aimed at academics, students and professional wildlife biologists and conservation scientists. Most of the workshops are taught in our purpose-built classroom in St Andrews, although we also organize courses worldwide on occasion. I have co-taught over 40 workshops to more than 800 people on distance sampling, estimating animal population density from passive acoustics, and statistical modelling.