This post is part of a series on student learning objectives (SLO’s) for both curriculum and courses. The SLO’s in this post are course level, specifically topical objectives 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).

The last post focused on high-level learning objectives for the course “Introduction to Data Science” (I’ve repeated them below for reference). Those are certainly the big picture, but those four objectives are hardly enough to really design day-to-day lessons around. Data 151 also has seven topical objectives tied directly to those general objectives and modeled after Paul Anderson’s DISC 101 course objectives. I’ll tie each topical objective back to the course’s overall goals.

**General Course Objectives:**

A. Students understand the fundamental concepts of data science and knowledge discovery

B. Students can apply and perform the basic algorithmic and computational tasks for data science

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

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

**Topical Objectives:**

- gain an overview of the field of knowledge discovery (A)
- learn introductory and state-of-the-art data mining algorithms (A,B)
- be able to distinguish and translate between data, information, and knowledge (A, C)
- apply algorithms for inductive and deductive reasoning (B,C)
- apply information filtering and validation on real world datasets (B,C)
- understand the social, ethical, and legal issues of informatics and data science (A,D)
- apply data mining, statistical inference, and machine learning algorithms to a variety of datasets including text, image, biological, and health (B,D)

Four of the topical objectives (1,2, 3 & 6) focus on guiding students towards understanding the fundamental concepts behind data science. One can hardly call a course an “introduction” without giving an overall picture of the field (**Obj. 1**) or spending time understanding key tools that practitioners use (**Obj. 2**). While I fully anticipate that the state-of-the-art algorithms will change, the basics like k-Nearest Neighbor, k-Means, and Decision Trees will certainly not. These algorithms provide a nice gateway into understanding the ideas of learning from a collection of data (**Obj. A**).

We also know in data science that what you can learn from a data-set is limited by the quality of the input data (like a lot of other things in life, garbage-in = garbage-out). **Objectives 5 & 7** articulate the sorts of data that will be used in the course, both real-world data and a mix of prepared/known data sets. These data sets provide a way to actually practice **Objectives 2 & 4** in more than just an abstract way. I want students to walk away from this class knowing how practitioners actually make use of algorithms. Students need to get their hands dirty putting some of those algorithms to work (**Obj. B/C**).

Now, I think it’s important to note here that in their projects and general work, I’m not expecting a really deep understanding or application of the algorithms. That’s saved for two later courses, one explicitly on data mining and the other their capstone sequence. In *Data 151* they should be learning enough to continue learning on their own, understand and interact with people who are really doing this work, and to grasp how the ideas can and are shaping the evolution of various disciplines or industries.

While **Objectives 2, 4 & 5 **articulate using data science skills, **Objectives 2-5 **have a second layer as well. These objectives aim to have students think about the implications and knowledge that comes from the data science process. This course is about more than just data engineering or data mining, it’s really about the questions and, well, science that can done with data. It is only when students can understand the processes of both inductive and deductive reasoning for science, or transform raw data into actionable knowledge that they become aware of the true power of the field (**Obj. B/C**).

Last, but certainly not least, **Objective 6**. As we know from Spider-Man (and some other great speeches), “With great power comes great responsibilities.” If you believe, like I do, that data science could dramatically change what we know and how industries and society is run… then I hope you are also a little nervous, perhaps occasionally terrified. Because if we DON’T talk about the social, ethical, and legal issues surrounding informatics and data science we might well end up with something like Ultron (the artificial intelligence gone bad in Marvel’s “Avengers: Age of Ultron”). More likely, we’ll end up with biased learning algorithms that perpetuate injustices or inequality. Making sure students have at least started to think about these sorts of issues may not prevent them from happening, but it is one (in my mind necessary) step towards that goal (**Obj. D**).

Together this is a pretty hefty set of things to accomplish in a semester. All in all though, I think they serve as a great lead into the entire field, and the overall goals of Valpo’s Data Science program (described in previous posts). Even if a student only takes *Data 151 *(as some certainly will), they will leave with a broad understanding of the field, enough knowledge to interact successfully with experts, and enough insight to see the real value that the effective and intelligent use of data can provide. I hope my business students are now prepared to be the “data-savvy business managers” that McKinsey & Co. described a few years ago and that the rest (C.S., Math and Stats) can work with, or become true data scientists, engineers, or creators.