What’s the Use of All This Data If No One Wants to Look at It?
Here’s a great question:
How do you get students who ‘hate’ math and aren’t interested in learning programming to learn the skills that will drive careers for the rest of the century?
Sometimes I think the answer is to trick them into it! Is there a puzzled look on your face wondering what I could possibly mean or maybe how it could even be ethical? Let me explain: we need to let the kids choose projects on topics that they’re passionate about. Once they’re excited about the project it becomes our job as mentors to guide them towards (and possibly teach them) the tools that will help develop in demand skills.
Today’s data jobs focus on practically every topic under the sun. Whether you’re interested in social justice, economics, finance, marketing, advertising, art, design, social interactions, or pretty much anything else there are public data sets that can be used as tools to learn about these topics.
A few great resources for these data sets are:
- Springboard’s post for data projects
- The Data Incubator’s posts on data projects (looks like there are currently six)
- Reddit’s r/datasets
- Kaggle’s datasets
- This GitHub repo
With plenty of data around, it should be easy to design a project that would be interesting to any student. As mentors and educators it’s our duty to guide them to explore the topics that interest them in new ways. How can we do that?
Ideally schools should add courses on data analysis. A full year course would be amazing to have but even a one semester course would provide a huge boon to the students fortunate to take it.
Of course with the cost of education increasing and budgets shrinking, it’s hard to get the faculty or other resources to do this. I would like to propose a more cost effective way for schools to provide access to the tools of the future to their students. Teachers and mentors need to start using project based learning (PBL) that is able to incorporate aspects of data analysis.
The wonderful aspect of incorporating data analysis into PBL is that it can easily be subject agnostic. I’m a physics and chemistry teacher so data analysis can be as simple as adding labs and incorporating Excel or Python into my lessons. But what if I’m a history teacher? Well there’s no reason that graphing can’t be part of the course. Take a look at a plot I made while experimenting with Python’s plotting libraries (this particular one was made using Tableau):
This graph is quite informative about the influence in Congress of American political parties and how they change over time. There was no technical analysis needed to make this graph but it provides a lot of insight to a topic that future Poli Sci students might otherwise miss.
How about a bit of economics or social justice? Last year the WSJ wrote an article saying that female CEOs had a higher median compensation with the implication that this was evidence against the gender wage gap. I explored some data that had CEO pay to see if this was true. I suspected it was just due to the size of the companies that the women were CEOs of and the higher median wasn’t indicative of a larger trend. This produced some interesting analyses with minimal need for serious statistics knowledge:
An instructor with some technical know-how or a really ambitious student could extend this exploratory data analysis into a model that predicts CEO compensation (data plotted as circles, model is the line):
Interested in predicting stock prices? That could be a project for a student motivated by the market! Here’s a short article about how to do that. Maybe criminology is your thing: crime information for Chicago. Do you want to be a meteorologist? Maybe you should take a look at this precipitation data.
But Andrew, I’m an art/design person! That’s great news because the design of data visualizations can benefit by someone with a knack for design and style! Here’s some information about the design and visualization/animation aspects. A poorly designed graphic can make learning information much harder.
The bottom line is that it isn’t hard to insert data analysis at some level into the work you already do. Creating open ended projects within each subject area is an easy way to augment what is already offered and provides the students with the opportunity to drive their project with their passion! If we can get them to that part then they’ll be able to figure out the rest.
Feel free to reach out to me if you want to discuss ways to incorporate data analysis into your courses or if you need help finding resources!
We want our students to be people who don’t have an aversion to graphs, tables and charts:
(Try looking at my portfolio of projects if you want to look at something else!)
Originally published at https://www.linkedin.com on February 12, 2018.