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Technical Short Courses and Training - Data Science and AI

The IDSAI will be sharing details of technical short courses and training related to data science and AI available at the University on this page.

If you have any suggestions or requests for courses please contact Jill Williams, IDSAI Senior Administrator at 

Please select one of the buttons below to find out more about the training offers currently available.

As part of Data Science Week 2020, Dr Oliver Stoner (IDSAI Research Fellow) delivered an Introduction to Bayesian Modelling for colleagues.

You can view the recording of this session here

As part of Data Science Week 2020, Dr George Dr Ath (Research Fellow in the Department of Computer Science) delivered an Introduction to Optimisation for colleagues.

You can view the recording of this session here

If you need some inspiration for how data science and/or AI can be applied to the research project / problem that you are working on, then take a look at some examples of DS / AI in action from our IDSAI theme leads below:  

Using Naturally Occurring Data to Understand Post-Natal Depression

Miriam Koschate & Elahe Naserian

Post-natal depression (PND) affects between 10-15% of mothers and about 10% of fathers, and can have detrimental consequences for the family as a whole as well as the new-born child well into their adulthood. Prospective longitudinal data are difficult and expensive to collect leading to many studies being either cross-sectional or retrospective. PND is still highly stigmatised making self-report answers less reliable. The rising use of online forums, such as Netmums, Mumsnet and Reddit, by millions of parents and parents-to-be creates anonymous naturally occurring, longitudinal data in the form of online posts. Computational analysis of such posts may provide a new way of examining changes during pregnancy and after birth.

Read more.

Uncovering Individualised Treatment Effect: Evidence from Educational Trials

Oliver Hauser & Zhimin Xiao, Charlie Kirkwod, Daniel Li, Benjamin Jones, Steve Higgins

The use of large-scale Randomised Controlled Trials (RCTs) is fast becoming "the gold standard" of testing the causal effects of policy, social, and educational interventions. RCTs are typically evaluated — and ultimately judged — by the economic, educational, and statistical significance of the Average Treatment Effect (ATE) in the study sample. However, many interventions have heterogeneous treatment effects across different individuals, not captured by the ATE. One way to identify heterogeneous treatment effects is to conduct subgroup analyses, such as focusing on low-income Free School Meal pupils as required for projects funded by the Education Endowment Foundation (EEF) in England. These subgroup analyses, as we demonstrate in 48 EEF-funded RCTs involving over 200,000 students, are usually not standardised across studies and offer flexible degrees of freedom to researchers, potentially leading to mixed results. Here, we develop and deploy a machine-learning and regression-based framework for systematic estimation of Individualised Treatment Effect (ITE), which can show where a seemingly ineffective and uninformative intervention worked, for whom, and by how much. Our findings have implications for decision-makers in education, public health, and medical trials.   

Read the working paper here.

Q-Step is a UK-wide £19.5 million programme to promote a step-change in quantitative training in the British social sciences. Exeter Q-Step brings together a range of activities related to training as well as curriculum development in quantitative methods. The Q-Step workshops in Applied Data Analysis seek to provide additional support to students interested in Quantitative Methods for the Social Sciences. They aspire to raise interest in Applied Data Analysis amongst undergraduates and postgraduates, and embed quantitative literacy in the culture of the University.  They provide a number of resources available to all colleagues:

Workshops - workshops are open to all. Upcoming courses include:

  • Introduciton to Bayesian Analysis
  • Bayesian analysis with JAGS/Topics in Bayesian analysis
  • Introduction to Open-Source Intelligence (OSINT)

For more information about these courses please visit the Exeter Q-Step site.

The Q-Step learning resources page - slides and captured workshops from Q-Step are available on the Centre's git hub page.  These are available for all colleagues to access.  Materials available include:

  • Computational methods: Python, R, SPSS, SQL, Nvivo
  • Quantitative research methods
  • Qualitative research methods

Stats Support Desk - Q-Step now provide an online Stats helpdesk service, available for colleagues to book appointments on Tuesdays between 1600 and 1800 (UG and MSc students will take priority at times of peak demand).  These appointments are held via Skype for Business and can be booked via:

National Centre for Research Methods - Academics across the UK, including in Exeter, have been awarded £2.8million until 2024 to help to support social scientists to conduct world-leading research through innovative training and capacity-building activities.

The training will be aimed at postgraduate students and early career researchers at any stage of their career, and will be delivered by staff at the University of Exeter Q Step Centre and international experts. The funding, from the Economic and Social Research Council, will pay for a comprehensive programme of cutting-edge research methods training across the UK. The training will be delivered through e-books, videos and interactive slide decks.  Check this page and the Q-Step page for announcements of training activities.

The Exeter Data Analytics Hub is a team of academics based at the University of Exeter across Exeter and Penryn campuses, who offer a range of workshops in the field of statistics, data science, machine learning, programming and more. They provide technical and analytical support for early career researchers based in at the University of Exeter. Courses include:

  • Python for Scientific Research
  • Introduction to R
  • Machine Learning
  • Spatial Data Analysis

For more information about these courses and workshops please visit the Exeter Data Analytics Hub site.

The Doctoral College provide a wide range of training courses and professional development opportunities available to postgraduate and early career researchers across the University.  Information about training opportunities can be found here

You can book on to all of the courses through My Career Zone.

There are a number of online resources available through the Doctoral College's ELE pages including:

  • Data Analysis Resources and Support
  • Data visualisation training and support
  • Software training resources for:

Where can I find R help and training?

Courses are occasionally run by Lauren Rodgers in the Health Statistics Team and the Bioinformatics Hub - Please check their events page for upcoming dates.

The Exeter R User group  runs user-group meetings featuring external speakers from both academia and industry, and has members of the group present their knowledge of R packages, best practice and specialist topics. The group is free to join and meetings are open to anyone with an interest R (regardless of their knowledge level) and are an excellent opportunity to network with other R Users.

The Health Statistics team also offer Stats Clinics, where a member of the  team will be available to assist with your Stats related queries. Attendees will be asked to complete a questionnaire prior to their Stats Clinic appointment, to enable the team to offer the best advice. Please check their events page for upcoming dates.