Many moons ago, I wrote some code to build a Tableau Data Extract from the work that I had munged together in python. I figured it was time to update the code since I recently discovered that the Tableau API has changed.
For a link to that old code, refer to the Jupyter Notebook in this repo.
Assumptions and Requirements First off, I am using a Macbook, and while I believe things are getting easier on Windows machines with respect to coding, I prefer to write Terminal commands over point-and-click installs.
I have been watching the DiagrammeR package for a while now, and at this stage, it’s pretty impressive. I encourage you to take a look at what is possible, but be assured the framework is there to do some really awesome things.
One use-case that applies to me is that of data modeling an app within Neo4j. There are already some tools out there, namely:
Arrows Graphgen by GraphAware And you can always use graphgists The last link above is a sample graph gist that is a decent overview.
I have been playing with Neo4j quite a bit, mostly for fun as I learn how I figure out when and where I could apply it to solve various analytics problems. Neo4j, at it’s core, is a database, which allows us to query data in a structured way. While the graph model within Neo4j is very flexible, the cypher query language is fantastic. Once you get over the learning curve, with only a few lines of code you can do some really powerful queries.
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.
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.
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.