Work integrated learning in statistics and computer science and fair assessment of authentic projects

Work integrated learning (WIL) has been the norm in disciplines such as medicine, teacher education and engineering , however it has not been implemented until recently in statistics and not for every student in computer science education. With the changed focus of universities, making graduates ‘job ready’ the collaboration of university-industry widened to encompass learning and teaching. Undoubtedly authentic problems coming from industry created opportunities for students to practice their future profession before graduation. However, this shift in the curriculum brought its challenges both for the students and their lecturers. In this paper, we will present assessment structures and case studies from statistics and computer science. Our approaches can be adopted or adapted by teachers of statistics and data science.