This session is facilitated by Emily Paremain, Ana Melro, Georgie Tarling, Michael Saunby
About this session
We offered students, from varying backgrounds, the opportunity to learn from experts in data science and machine learning at a three week residential summer school. In this session we will describe our approach to developing more inclusive learning experience for Data Science and AI. We will present and discuss findings from the 2018 and 2019 Institute of Coding Summer Schools at the University of Exeter and share examples of teaching practice around programming, data science and AI. Other things to discuss. • Why run short courses on programming, data science, machine learning? e.g. What needs are being met?, Who are these courses for? How do they benefit? - including networking, How to reach others with curiosity about tech? • What needs are not being met, but could be? • Short courses don’t typically ask participants to disclose learning, or other, disabilities. Should we, and why?
Goals of this session
By participating in this session you will discover what has motivated students from a variety of disciplines, with no previous exposure to computer science, to learn about data science and machine learning. Technical and ethical decisions are being made every day in businesses and organisations of all types, often assuming that recent graduates are well equipped to advise in these important decisions. What knowledge is needed to contribute to projects, debates and decision making when technologies such as machine learning are applied in education, social care, politics, entertainment or any other fields? We have developed an enjoyable three week course to introduce those with no previous programming experience to data science and machine learning in Python and R.