The Prismatic Team has slowly been rolling out a very cool API. You can read all about it here. At the same time, I have been using this as an opportunity to learn how to create an R package.
After today’s API update to identify the relevant content related to a specific topic, I wanted to highlight what is possible with a few lines of code using the prismaticR package.
TL:DR Below I use Prismatic’s API to tag the mission statements of approximately 500 colleges in the U.S. in order to evaluate the “focus” of each, which I define as the topics extracted from the API. In addition, I also consider the competitive nature of various schools, commonly referred to as the “Competitor Set”.
Out of the gate, I considered more than 1,000 schools for this study. In the end, I only kept institutions with clean data, that is, no missing information across all of the data I collected.
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.
We were talking about admit rates at work, and as luck would have it, I saw the tweet below coming out of the Eduventures conference basically the same day.
More than 70% of American colleges accept everyone who applies. Only 200 colleges accept less than 50% of applicants #eac14 — Karlyn Borysenko (@KarlynMB) June 5, 2014
Because we can download full survey datasets from IPEDS, it’s fairly simple to hack something together fairly quickly to test this ourselves.
As of late, there has been a surge in conversation around the topic of the college-going population here in the United States.
One one hand, we have long talked about the “The Perfect Storm” of demographics. For example, here is a simple Google Search. On the other, the decline in college enrollment, has been connected to changes in the labor market.
In the end, it might be nice to review what data exist and highlight how these flashy headlines could have been predictable well in advance of 2014.
Reaction: Netflix-like Recommendations for College Earlier today I saw the following post:
Could a Netflix-like algorithm be the key to matching students to the right college? http://t.co/6oPgM62zA9 #HigherEdData #collegeaccess — Chronicle Data (@chrondata) January 23, 2014
Before I go deeper into my thoughts on the matter, I want to give a hat-tip to Dan Jarratt for actually deploying his idea. Well done sir.
Dan is on to something and I commend his approach.
This is a quick document aimed at highlighting the basics of what you might want to do using MongoDB and R. I am coming at this, almost completely, from a SQL mindset.
Install The easiest way to install, I believe, is
library(devtools) install_github(repo = "mongosoup/rmongodb") Connect Below we will load the package and connect to Mongo. The console will print TRUE if we are good to go.
library(rmongodb) # connect to MongoDB mongo = mongo.
About the post Just like in the previous entry, we will be using R to access our school’s Google Analytics data through their API. In this post, I want to highlight how we can figure out when a vistor to our website completes our a goal on our site. In my case, I am interested in learning more about how, and when, prospective students (and/or parents) complete our information request form.
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.