Students have varying experiences learning computational techniques. For some, the math and programming that are the foundation for modern data science techniques come naturally. For others, being introduced to these concepts can be an anxiety producing experience. I am fond the phrase “your mileage will vary” for describing these differences - no two students have the exact same experience taking a computational methods course. While some of this is specific to this course, the general outlook on approaching data science work that I describe below is hopefully applicable in a far wider arena.
Zen and the Art of Data Analysis
One of the biggest challenges with this course can be controlling the anxiety that comes along with learning new skills.
R syntax, GIS terms, and Markdown can seem like foreign alphabets at first. Debugging
R syntax can be both challenging and a large time suck, in part because you are not yet fluent with this language. Imagine trying to proofread a document written in a language that you only know in a cursory way but where you must find minute inconsistencies like misplaced commas.
For this reason, I also think it is worth reminding you that many students in the social sciences struggle with computational methods at first. It is normal to find this challenging and frustrating. I find that students who can recognize when they are beginning to go around in circles are often the most successful at managing the issues that will certainly arise during this course. Recognizing the signs that you are starting to spin your wheels and taking either ten minutes, an hour or two, or a day away from computational coursework is often a much better approach than trying to power through problems.
Data analysis therefore requires a certain mindfulness or what I call “habits of mind and habits of method.” These mental habits extend past being able to recognize that frustration is setting in. They also include the mental habits needed for Getting and Staying Organized and strategies for Getting Help as you navigate the inevitable errors that come with learning new analytically skills.
Getting and Staying Organized
Doing data science work, and having the space to step away for a day as the last section suggests, requires discipline and organization. Similarly, computational coursework can demanding not just because it is complex but because the courses often have a number of moving pieces that you need to keep track of. Being mindful of this challenge from the beginning, and taking steps to plan for it, is an important part of this course.
Keeping Track of Where You Are
Students who have some system for tracking their work and creating to-do lists are often the most successful in this course, not because they have a fundamentally better grasp on the content but because they simply are more organized. If you have never thought particularly hard about how you manage tasks, now is an excellent time to start doing so. You do not need fancy computer software to accomplish this, though there are an array of possibilities if you do want to use software to keep yourself organized. A legal pad or a notebook can be just as effective as a $50 to-do list manager. The point is, do something!
I am fond of recommending the Getting Things Done methodology to students as part of thinking more holistically about staying organized. The website Lifehacker posted an excellent introduction to GTD that is a great way to get a sense of how it works and find additional resources for implementing it.
The GTD website has a great list of software for those of you looking for a to-do list application. One that isn’t listed that I have used for collaborating with my student research team is Trello, a freemium website that allows you to create simple to-do lists. It isn’t sophisticated enough for implementing GTD, but it is more than sufficient for managing to-do lists related to this course.
Virtual Office Hours
We don’t always do a great job of explaining office hours to students, so I’ve put together an explainer on my website to lay out what office hours are for, when you should come, and remind you that it is OK to stop by without an agenda!
For the 2020-2021 academic year, there are two types of office hours that I have:
- Appointments: Appoints are available for an hour each week from 9am CST to 10am CST on Wednesdays. You can sign-up for an appointment by visiting my Calendly page. Select either a 15 minute or 30 minute meeting, and fill out your details. Once you submit the meeting request, you’ll receive a calendar invitation and a Zoom link.
- Drop-in: From 10am CST to 10:45am CST on Wednesdays, I’ll be available via Zoom for drop-in office hours. No appointment is required, and you’ll be able to come to a dedicated Zoom meeting that has a “waiting room.” I’ll bring one student in from the waiting room as a time so that we can meet privately. The link for this is available on Blackboard.
If these times do not work for your class schedule, please email me to set-up a meeting at another time! I’ll work with you to find a time that works for both of our schedules.
In addition to my office hours explainer, I have the following articles on my website: