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This month we'll be hearing about student driven peer-learning, and teaching research computing to a multidisciplinary audience. Abstracts below.

Student Driven Peer-learning in Biomedical Data Science

Dr. Tim Ebbels and Valentina Giunchiglia from the Department of Metabolism, Digestion and Reproduction will be talking about a student-led initiative, called Data Science Helper Team, organised within the MRes in Biomedical Research.

The MRes in Biomedical Research is the largest MRes in the Faculty of Medicine, with 80 students spread across 8 subject-specific streams. Students work on a diverse range of research projects from basic wet labs to data science, and it is perhaps no surprise that some students on this diverse MRes need support to acquire data science and bioinformatics skills. To help their fellow students, a couple of students in the Data Science cohort of the MRes have set up the Data Science Helper Team.

The Data Science Helper Team initiative consists of peer-to-peer learning in the field of biomedical data science. As part of this activity, lectures and Q&A sessions are given by a group of students from the data science stream to students from the other streams of the MRes. The Data Science helper team provides important evidence of the advantages that peer-to-peer learning can bring to students as both learners and teachers, especially across a highly multidisciplinary cohort of postgraduates.

I-Explore STEMM - Teaching research computing to a multidisciplinary audience

Module team: Jeremy Cohen, Christopher Cooling, John Pinney, Jianliang Liam Gao, Tony Yang, Eimear O’Sullivan, Tom Hodson, Yiannis Simillides and Katerina Michalickova

We will talk about designing and delivering Interdisciplinary Research Computing - an undergraduate module, which was part of the first group of I-Explore STEMM modules. The staff from the Graduate School Research Computing and Data Science Programme together with the student partners, GTAs and Jeremy Cohen from DoC worked together on the module. The course introduced 2nd year undergraduates from various departments to a range of technical topics and ways of thinking that are important when undertaking a scientific project with a substantial computational component. The module was designed in accordance with the I-Explore mission to provide a low-risk environment for students to learn about novel topics through exciting pedagogies. The first instalment of the course was delivered remotely last term to 24 students from Medicine, Life Science, Mathematics and Physics who commented positively on their experience of the module and the teaching methods.