This post is part of a series on student learning objectives (SLO’s) both for curriculum and courses. The SLO’s in this post are curricular level and address soft-skills and versatile expertise. Love them or hate them, student learning objectives are a part of higher education (I for one appreciate how they provide focus for curriculum and courses).
Data science is acknowledged as an interdisciplinary field where a practitioner must have both broadly transferable skills ( data skills that apply to any data problem) and specific skills/knowledge within domains that questions arise in. So, a successful data science graduate must have domain knowledge, but it could be ANY domain knowledge. Moreover, they need to not only have domain knowledge but be able to actually transfer the data skills they’ve learned in the program to actually solving domain problems. And we need a student learning objective that captures that. Therefore, our third SLO is:
3. Students will be able to apply the skills and methods of the major [data science] for problem solving in data-intensive fields such as economics, meteorology, and biology, among others.
This objective lets us both capture the need for learning data skills to the point of transfer without specifying exactly what fields the student actually transfers their skills to. Since Valparaiso has its roots in a liberal arts tradition, this was particularly important since we want to encourage students in any discipline at all to consider adding a data science degree. While there are obviously more ‘traditional’ data-intensive fields (as listed), there are projects in any discipline that can make use of data skills, even as esoteric a field as studying historic french cookbooks (we have a computer science student helping with that project…). In practice, this objective can be met by students in a variety of ways, but most often by adding a minor or taking one of a specific set of data-centric discipline courses.
The final curriculum SLO I want to discuss deals with a (in one sense) completely non-technical skill, but stems from Valparaiso’s core values as grounded in the Lutheran tradition.
4. Students will be aware of and engaged with the use and misuse of analytical and statistical data-derived conclusions in the wider world
The news recently has covering many issues related to ethical use of data. Cathy O’Neil’s book “Weapons of Math Destruction” also highlights several cases of significantly unjust uses of data or data models. The Philosophical Transactions of the Royal Society even released a special themed issue in Dec. 2016 on ‘The ethical impact of data science‘. As a religiously based institution, Valpo cannot simply ignore training students in the potential pitfalls of building data models. But, even beyond that I believe that across the field, data scientists must think about, and understand, the moral, ethical and social impact of the projects they work on. I believe every member of our society has a personal responsibility to act in ways that support the common good, even if/while they are making decisions to promote their own good. Without proper training it is far too easy to overlook the possible bias inherent in collected data, designed models, or even the implementation of action in response to discoveries. The impact from a less than thoughtful choice or model could severely impact hundreds, thousands, or even millions of people. I certainly don’t want look back and realize I had trained that data scientist on my conscience…