From April 20, 2017

Student Learning Objectives (Part 1)

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).

While there definitely is still debate about what exactly should be included in a full curriculum (Post forth-coming on the ASA’s guidelines), there’s definitely some consensus on the basic knowledge needed to succeed. That consensus centers around (1) needing a mix of hard-skills between Mathematics, Statistics and Computer Science (MS-CS) and (2) that data scientists (or really students in general) will need a variety of soft-skills. For example, respondents to O’Reilly’s 2016 Data Science Salary Survey reported that of the tasks they had a major involvement in 58% spent time “communicating findings to business decision makers”, 39% were “organizing and guiding team projects” and 28% had to “communicate with people outside [their] company”. This leads to the first SLO:

1. Students will demonstrate the ability to communicate effectively about mathematical and statistical concepts, as well as complex data analysis, in both written and verbal formats.

Phrase that SLO however you like, but I suspect you’ll find nearly all data science programs will need to have something along those lines. Our next SLO focuses even more on the hard-skills. We know our students will need technical skills including programming, software design, and experience working with big data. However, if you are reading this blog, you probably are aware enough about the high volatility and rapid changes that are occurring in the business and academic world of data science. Significant software workflows exist now that didn’t exist 6 months ago (heck, probably 2 months!) for answering data questions. Taking these into consideration, our second SLO addresses the skill need however phrased in a very generic way so that it will maintain its long-term viability.

2. Students will be able to implement solutions to mathematical and analytical questions in language(s) and tools appropriate for computer-based solutions, and do so with awareness of performance and design considerations.

With these two objectives, from our perspective we’ve covered all the generic skills that a student with any data science degree is going to need to be successful at. Part 2 will tackle SLO’s that relate to application knowledge and ethics.