This repo contains my first-ever R Shiny project. It’s simple, and represents a minimally viable app. It’s super basic, but the app allows us to query and visualize the NHL’s Play-by-play event logs for a given game.
I updated the app for the 2015-16 season. There are a few manual updates to the code that I could refactor and allow the end-user to set, but in the short run, it works.
This repo contains the code to replicate the Scannel and Kurz Post on why huge swings in app growth might not always be good for Enrollment Managers.
One important note. I have no affiliation with S&K. I simply wanted to build upon a common idea and produce code so others can run and build upon this very important concept in Enrollment Management.
About I have been collecting and analyzing data along the lines of the S&K and post for a while now, but they beat me to the punch and did a great job highlighting an important point.
Intro The use of graphs to solve business problems is not new, as companies like Amazon, Netflix, and nearly all major social media sites have been doing this for some time. I have been obsessed with graphs for just as long, and after learning as much as I can about analysis of graphs and graph databases, I am finally getting the time to take what I have learned and apply it to real world data problems I face within Enrollment Management.
TLDR; Enrollment Science is my attempt to relate the emerging role of Data Scientists to Enrollment Management Divisions within Higher Education. Let’s get real. Higher Ed is BIG business, and it’s about time that we start to embrace advanced techniques (i.e. the methods industry has been using for many years now) in order to be more efficient. We need to think differently about how we run our organizations, and in my opinion, it starts with the principles of Enrollment Science.
Abstract: A simple example of predictive analytics for Enrollment Managers using FREE tools.
TL;DR: Using R we can fit all sorts of complex models in Enrollment Management, quickly, and for no cost. In truth, data modeling can help undercover complex relationships at your school that are not easily visible in our usual tables and charts. However, predictive analytics is not the golden ticket to enrollment success. You will need to understand not only what the model is telling you, but also the risks associated with being incorrect.