Data Science for Engineers: A Teaching Ecosystem
Felipe Tobar, Felipe Bravo-Marquez, Jocelyn Dunstan, Joaquin Fontbona,, Alejandro Maass, Daniel Remenik, Jorge F. Silva

TL;DR
This paper presents a comprehensive ecosystem for teaching data science to engineers, integrating theory, methods, and applications through a collaborative, multi-departmental approach to meet growing industry and academic demand.
Contribution
It introduces an innovative, multi-faceted teaching ecosystem for data science tailored for engineers, developed collaboratively across university departments.
Findings
Established a multi-departmental data science teaching ecosystem
Integrated postgraduate programs, courses, and practical labs
Shared teaching principles and innovative approaches
Abstract
We describe an ecosystem for teaching data science (DS) to engineers which blends theory, methods, and applications, developed at the Faculty of Physical and Mathematical Sciences, Universidad de Chile, over the last three years. This initiative has been motivated by the increasing demand for DS qualifications both from academic and professional environments. The ecosystem is distributed in a collaborative fashion across three departments in the above Faculty and includes postgraduate programmes, courses, professional diplomas, data repositories, laboratories, trainee programmes, and internships. By sharing our teaching principles and the innovative components of our approach to teaching DS, we hope our experience can be useful to those developing their own DS programmes and ecosystems. The open challenges and future plans for our ecosystem are also discussed at the end of the article.
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