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 course level, specifically for an *“Introduction to Data Science”* (Data 151) class for new students. 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).

In many ways, the general course SLO’s for Data 151 mirror the SLO’s for the program as a whole. Students need to leave with an understanding of what data science is, know about the basic algorithms, and be made aware of the ethic and moral issues surrounding the use of data. Data 151 is intended to be a hook that draws in students from across our university to learn about data and then consider adding a major in Data Science. It also draws in juniors and seniors in less technical disciplines like business. This may in turn make Data 151 the only course where a student explicitly thinks about data. The major difference between the curricular and course SLO’s is the depth of understanding I expect students to leave the course with (as opposed to the program). This is most clear in the first two SLO’s below.

- Students understand the fundamental concepts of data science and knowledge discovery
- Students can apply and perform the basic algorithmic and computational tasks for data science

As said, these are very close to the first two SLO’s for the whole curriculum and related to both their ability to communicate data science concepts and also their ability to implement solutions, though in both cases with lower levels of expertise. Data 151 has two additional SLO’s that target the broader (potential) audience for the course (in addition to continuing majors). These are:

3. Students develop and improve analytical thinking for problem formulation and solution validation, especially using technology

4. Students prepare for success in a world overflowing with data.

In many cases, students in *Intro to Data Science* are still gaining experience (aren’t we all?) with general problem solving skills. Perhaps (to my mind) one of the most under-taught skills in STEM courses is how to actually formulate and structure the **process** of solving a problem. In many, many cases, a significant amount of time can be saved in the execution of problem solving by carefully planning out **how** you are going to explore or solve a problem. Data science even has this explicitly built into several locations in a typical workflow, specifically performing exploratory data analysis and planning for solution validation.

Meanwhile, the final objective is meant to really be a catch-all. The field of data science is changing incredibly rapidly, as are the ways data is generated and used. I wanted Data 151 to be something that is capable of covering current, bleeding-edge topics. This SLO also nicely encompasses my plans to bring in alumni and current practitioners as speakers to give the students insight into what future jobs might look like. Bringing in these speakers also provides a chance for students to get an industry perspective on workflows and processes, something that can be very different from academia’s problem solving process.

These SLO’s are pretty high-level, but intentionally so. At Valpo, we’ve got both “course objectives” and also topical objectives. My next post will take a look at the specific, topical objectives for Data 151, which deal with the more nitty-gritty topics of what will actually get covered in Data 151.