Here’s a rather unconventional commencement address from Mike Rowe:
I like both his gentle irreverence and the simple truths of his points.
How do you succeed professionally? Mr. Rowe makes some suggestions:
Practice your craft everyday.
Become indispensable to your employer.
Show up early.
Stay late.
Distinguish yourself on the job at every opportunity.
No matter what your child does after high school, these are solid points to be successful.
If you’re a skilled tradesperson with an entrepreneurial spirit, a willingness to get dirty, a disposition to travel, and a burning curiosity to learn all that you can…I’m telling you, your opportunity to prosper has never been better.
Mike Rowe
I think it’s great that he references an “entrepreneurial spirit”…I wish he would have repeated that point a few more times. It shouldn’t be a given that we all must toil for an employer, when there are opportunities to be your own. Along with equally giving voice to the trades, it would be great if our educational institutions would give ample voice to entrepreneurship, as well.
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:
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!
LifeHacker published an article recently called, How to make more money in 2019. Basically, the article surveyed five people, collected some of their financial particulars, and asked what their plans were for earning more money in 2019. Here’s the short list of money-making strategies I gleaned from the article:
Work a second job
Reduce frivolous spending
Establish and stick to a budget
Acquire more skills (programming, negotiating, etc.)
Acquire certifications, graduate degree, etc.
Take on more work responsibilities in hopes a raise will follow
Find a new job that pays more
Increase your financial literacy through reading and research
Set meaningful financial goals
The salaries, jobs, and ages of the interviewees lined up like so:
The age/salary progression seems reasonable: the older interviewees tended to earn more than the younger ones.
The Applications Engineer resides in Michigan, the Data Specialist in Portland, Oregon, and the rest in California. Given that California and Oregon have some of the highest costs of living in the country, I just don’t see how the four that live in those states can fare on those salaries, particularly the Data Specialist:
The interviewees also estimated their expenses versus savings. Here’s what I gathered from the article (I couldn’t get a good estimate on the expenses of the Applications Engineer, so I left her out):
First, it seems to me that we’re not hearing the full story from the Data Specialist or the second Writer. You’d have to be pretty extraordinary to be saving 40% of your income let alone 75%!
More interestingly, the average income they listed in the report was pre-tax. As taxes tend to constitute the biggest expense of households, I would expect that savings slice to shrink even further.
A few of the interviewees mentioned that they’re still paying off student loan debt, which, sadly, seems all too common these days. In particular, the Data Specialist, who’s working for a non-profit in Oregon, is working off some $60k in student loan debt. That combination of factors makes my head hurt.
All said, as a parent, I must do what I can to a) beef up my own financial literacy and b) pass what I know on to my kids so they’re as financially prepared as possible.
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