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Doing Data Things

Is this thing on? 😉

I’m not going to apologize for not posting for, um, almost a year. As you can see from my last post, which I really just recommend skipping and taking this lesson from: 2017 was extremely hard on me. It turns out, 2018 hasn’t gone especially well, either, in many ways that I don’t want to talk about right now. Instead, I want to talk about one specific thing that has been very good: I’m finally on a road toward using my data skills professionally!

I’ve mentioned that I’m an adjunct for the local community college, working as a reference librarian. That is not where I want to be, career-wise, for a number of reasons. (I’m not demeaning my coworkers or the college’s students. Rather, “adjunct” and “reference” both really need to be temporary appellations for me. I’m a technical person: give me your website or your metadata or, heck, even some spreadsheets, I’m not picky; have me teach people how to use technology; let me shepherd through policies about shared software usage; even have me answer chat and email questions from patrons; just, please, never make me physically sit at the reference desk, because I hate almost everything about that experience.) Really, I want to be working with metadata or data, which is pretty funny when you think about how much of my first masters degree I did in MATLAB; I should have stuck with that, I guess, rather than … all that other stuff I did afterward.

Anyway, when the college announced a Data Analytics Certificate for [post-bachelors] professionals, you bet I used my free tuition to sign up for as many classes as I could! (Two. We get two classes per semester.)

So this semester, my Monday nights were spent in DAT-102, Introduction to Data Analytics. It was a 20,000 foot intro course, so we didn’t get into a lot of depth on anything or cover that much that was new for me—the stated goal was to give students an opportunity to decide whether data analysis was a good path for them or not. I did learn some Excel tricks (and relearn how to do pivot tables, for probably the third time, because I keep going multiple years at a time without needing them—which really just points to bad life choices, doesn’t it?), and I picked up a couple of new data sources to play with. I also finally understand and can explain p values, which was not a thing my engineering-focused probability and statistics courses ever really covered. (Should I not admit that? Eh. We all learn things that “everyone knows,” every day.) It was also a fun class, because the professor broke up the 3 hour(!) time block with activities to try to build up students’ intuition about things like probability distributions and experiment design. It was a good experience.

Tuesday nights were CIT-129, Python 2. Yes, I minored in computer science, but it’s been *cough* more than 10 years since then. And yes, I’ve written Python professionally, but only for a very short time, under very bad circumstances, which didn’t really enhance my learning much. So, again, I was in a course where we didn’t really go over anything I hadn’t seen before, but we went over things formally! Which was great! I’m definitely a better programmer for having taken it. Plus, it was a small class, everyone in it was really good, and our professor (same professor for both classes) was a fantastic teacher, so we went so much faster than the original syllabus had suggested we might. I really enjoyed it! Our final meeting, where we shared our semester projects, was last night, and I’m pretty sad that it’s over. Sadder still: there is no Python 3.

One really good outcome of taking two classes that I was over-prepared for is that the professor noticed and suggested I might teach Python 1 next semester and, much more surprisingly, team-teach DAT-201, Data Analytics 1 (the next class in the series), with him. The big themes of DAT-201 are “mapping” and “forecasting,” neither of which I’d say I’m expert in, but I have enough background that I can absolutely be ready to teach it (team-teach it, I mean; I don’t even have to teach it alone!) by the time next semester begins. Python 1 is less intimidating: I’ve taught intro to programming before, only it was online (NEVER AGAIN*) and in JavaScript instead of Python. I think Python’s a nicer teaching language, and I certainly enjoy it more.

One cool thing about all of this, assuming that no one comes along and bumps me from Python 1 (adjuncts can always be bumped) and that the dean signs off on my colleague and me splitting Data Analytics 1 (this is likely but has not yet happened, that I know of), is that I’ll get to have a major impact on a program that is both new and fairly unique. Not a lot of community colleges are doing data analytics, yet. Ours is offering two versions of the program: an associate’s degree (a normal enough thing for a community college to offer) and that post-bachelors certificate (a bit less common of an offering). There’s an official list of courses with descriptions on the college website, but it was put together before they hired anyone with a lot of data expertise. (Makes sense: you can’t hire the data faculty until you have a data program!) Now they have a couple of data-focused faculty members (more, if you include adjuncts like me), who I think have made some updates to the high-level program, but when it comes to really detailed planning (e.g. syllabi), the courses are still very much in development. “We are building the ship as we sail,” my colleague says. … Which is a lot of potential for me to have an impact! (For instance: we will be covering data ethics in 201. And hopefully every other course in the program. But definitely 201, if I’m helping to write the syllabus!)

I’m pretty excited about it! And, yes, I did say adjuncting isn’t something I want to do forever, above. Of course, few people do! But adjunct teaching is a better gig—both in terms of how much I can help students and, probably, in pay (adjunct instructors are in a union; adjunct librarians are not)—plus, adjunct-teaching these classes will be a lot of fun. And challenging: both classes are being taught in three-hour chunks one night a week! Also, with only 1.5 classes, I’ll still be able to work at the library for a reasonable number of hours per week, which is nice, because we’re implementing most of the Springshare suite over the next few months, and there’s a lot I can contribute to that, as well. (I’m a reference librarian and just an adjunct, sure, but our web librarian has too much to do. Which means sometimes fun projects trickle down to me! I make sure to get scheduled time at-work-but-off-desk each week, so that I can do projects.)

I do have a couple of job applications open right now, and if you happen to be a potential employer who has read this far, 1) thanks(!), and 2) I want to point out: both of the classes start at 6pm, rather than taking place midday, so we can make it work. I’m also going to prepare really thoroughly during December and early January, to make the load lighter during the semester.

* I would absolutely teach online again, but most students in their first programming course need some dedicated in-person time. Also, the expectations that many students seem to have about online courses are not really compatible with something as work intensive as learning to program. (May vary by degree?)

Published inccacclasseshiring and employmentlibrarianshipprogrammingpythonteaching and learning

One Comment

  1. CreepyPeepee

    Umm.. Where are you!

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