Enrollment Numbers at Michigan State University

As an alumnus and graduate of MSU, I wanted to make a viz about my alma mater. Just the other week, I read a press announcement regarding MSU's enrollment numbers had almost eclipsed 50,000 bodies, which really surprised me for some reason. I knew MSU (and UM as well) were both big schools, but never thought that they were growing this fast and getting as big as the other notoriously large schools such as University of Texas and Arizona State University. To give a direct comparison, MSU had 35,478 undergraduate students this past Spring 2014 semester, UM had 26208, and ASU had 59,382 undergraduate students. Yes, you read that right: ASU had as many undergraduate students as both MSU and UM combined. 

But I digress; this post is about MSU, not UM or ASU.

For the viz, I was able to pull data going back to Spring 1995 - I somehow forgot to grab data for Fall 1994. MSU started recording enrollment numbers differently that semester, so the data looks a bit different before then. I pulled the data from our Office of Registrar. I highly recommend poking around the reports if you're curious yourself. 

And without further ado, here is the viz:

Learn About Tableau

A couple of points:

• I love the College of Engineering's cosine wave-esque graph (had to double-check it wasn't the sine wave, which starts at 0,0)
• Enrollment is always higher in the fall semester than the spring in a given academic year. I tried doing some further research on this, but couldn't find much. The only plausible explanation I was able to find was that students, especially freshmen, are likely to drop out or transfer after the fall semester, rather than waiting.
• The Lyman Briggs, Natural Science, and Veterinary Medicine Colleges are the 3 that have maintained the same size over the years. 
• On the other hand, the College of Business has grown to take the title as the largest from the College of Social Science
• It will be actually about 5 or 6 years until MSU averages 50,000 students for the entire academic year

    
    Making this viz wasn't as complicated as NFL Away Games. I learned R earlier this month and it definitely came to use, as I had almost 40 CSV files after manually downloading them. I made a simple R package to grab the data from each file and spit it all into a new one, while also formatting it a bit. Like anyone else in the data analytics/visualization field, I highly recommend learning to use R. I would label it as a stripped down version of Python but geared for statistical + data analysis.
    I also ended up using Tableau's Excel add-in which is actually pretty neat. I thought it was somewhat pointless when I first added it to Excel, but it proved to be a nice tool in sorting data for each class. 

Go Green!

Away Games for NFL Teams Visualized

For my first post, I wanted to create a data visualization that didn't require much statistics capturing trends or anything - just something easy and interesting. The first thing that came to mind was mapping out how far each National Football League team traveled during the 2013-2014 regular season. 
So I got a 10-pack of Capri Sun, sat down, started chugging away at some data, struggled with formatting it, received tremendous help on the Internet, and ultimately came up with this viz:

Learn About Tableau

Some things to note:

• There is absolutely no correlation between less-distance traveled and a better record. I actually didn’t even bother analyzing this since the Seahawks, who were NFL champions, traveled the most of any team
• It is interesting to see the Seahawks and 49ers, arguably the top teams from last year, in the top 3 for distance traveled
• Along those lines, the NFC West traveled the most as an entire division. 
• The AFC North traveled the least as a division
• The Cleveland Browns have to travel the least to play their division opponents in the AFC North
• The Seattle Seahawks have to travel the most to play their division opponents in the NFC West
• If there's anything else, feel free to comment

Making this viz was difficult and time-consuming, but still really fun. It took a while to get all the data from different sources lined up and formatted properly. While I was asking around on Twitter and Tableau's community forums, others told me they had re-arranged my data even more, meaning that I hadn't even done it properly. So a big takeaway for me was to spend more time on data cleansing and formatting, even if you have what it needs to start making the viz. 

I wish I had included some more info-graphics in this viz, but I just wanted to get this published and was content with the end-result. If there are requests for a particular info-graphic, I will try my best to edit this viz and post

A big thanks to @DataRemixed , @awesomepeter, and Noah Salvaterra on the Tableau forums for helping out.