A few months ago, Machine Learning Plus published a great article demonstrating the power of matplotlib by showcasing 50 cool visuals you can accomplish with the package. Inspired, I wanted to see if I could replicate some of these visuals, but with data I’m interested in.
So, I started with their bubble chart, but instead of using the strange, Midwest data they used, I thought I’d work in a space that’s been preoccupying my time of late: college tuition. What sort of bubble chart could I craft that depicted college tuition in some way? What about a bubble chart depicting the intersection of college tuitions and their corresponding average starting salaries? That might help parents and students better understand the return on investment associated with various colleges. Here’s what I came up with:
First, I decided to narrow down my work to just Ohio colleges. At Payscale.com, I found a dataset of median starting salaries by Ohio college for 2018.
Unfortunately, the Payscale.com dataset did not include college tuition prices. However, CollegeCalc.org did have a dataset of Ohio college tuition prices for 2018-2019.
Much of my work revolved around cleaning up these data sources and merging them together for the final visual. As you might imagine, each dataset tended to have slight name variations between schools. For example, the Payscale.com dataset had an entry for Kettering College whereas the CollegeCalc.org site calls that school Kettering College of Medical Arts. So, I had to do a fair amount of work making sure both datasets called each school the same name so that I could properly match on those names.
The Payscale.com dataset included some language to differentiate public schools from private, which I used to color my bubbles blue and red, respectively. The CollegeCalc.org dataset included the school size which I used to size each bubble.
Machine Learning Plus’s bubble chart includes a cool “encircling” device that draws a circle around certain datapoints to draw the user’s attention to those points. Instead of doing that, I thought it’d be interesting to draw a “break even” line. All things equal, if you pay, say, $10,000 in tuition for 4 years, you’re tuition investment would break even if your first job out of school paid $40,000. I drew a line to that effect on the graph: datapoints above that line would have a positive return on investment whereas datapoints below that line would have a negative return on investment. I didn’t want to muddy up the chart labeling each bubble with the name of the college, but I still thought it’d be fun to calculate which schools are above and below the line, so I found a way to do that, added the calculation as a column to the dataframe, and printed out the Top 5 “Best” returns on investment and the Top 5 “Worse” returns on investment.
Top 5 biggest ROI schools:
68 Central State University
40 Kent State University at Salem
45 Kent State University at Trumbull
56 Kent State University at Ashtabula
46 Shawnee State University
Name: School Name, dtype: object
Top 5 least ROI schools:
8 Oberlin College
1 Kenyon College
3 Denison University
18 The College of Wooster
6 Ohio Wesleyan University
Name: School Name, dtype: object
Obviously, my “break even” assessment is very simplistic. There are many other variables I don’t account for: room and board, fees, financial aid, merit scholarships, taxes, and the like. The median starting salaries are across all graduates from a given school–from Philosophy majors to Computer Science. So, your mileage will certainly vary. For me, the bigger take-aways were 1) the challenge of obtaining, cleaning, and merging the datasets, 2) charting out the results in a cool way, and 3) calculating the datapoints above and below my break-even line. All my work is here in case you want to check it out. Look for more matplotlib charts inspired by the Machine Learning Plus article in the future!
You could be doing this whole college visit thing for up to a year or more. By the fourth or fifth school, you may even begin forgetting the earlier ones. Aside from taking decent notes on each school, you should objectively score them against criteria that’s important to you. For example, if graduating in the smallest amount of time is important to you, you might ask yourself: “I have a strong likelihood of graduating in four years from this school.” Then, based on the information you’ve gleaned from you visit, you might score that statement from one to five with five being the highest likelihood. Come up with a variety of similar questions that reflect what’s important to you in a school and put them in a scorecard. Either during the visit or shortly after it’s over, have your child fill it out. Later, record those scores in Google sheets or other spreadsheet software. Eventually, you can tally those scores to see which school scores highest.
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