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.
Over the last few weeks, I have begun tagging posts on Twitter with the
hashtag #emsci. It’s about time that I discuss what I believe to
be Enrollment Science. As far as I can tell, this
the notion that:
Data is the new oil
That was back in 2006. In 2012, this Forbes
struck a chord. The concept of “Data Science” has been gaining
tracking ever since.
What is #emsci all about?
At the end of the day, there is both an art and a science when it comes
to Enrollment Management, the term coined by Jack
Maguire. As an
industry, not only do we collect an immense amount of data internally,
but we report extremely detailed information to a wide range of external
audiences. Outside of public companies listed on stock exchanges, no
other industry is required to publicly expose this level of data. It’s
about time that we begin to use these datasets more effectively. I am
calling this Enrollment Science, a play off the wildly over-used
term, Data Science.
used the venn diagram below to describe the skills required of today’s
It’s not surprising that the emphasis on leveraging data would make
it’s way to higher education. NACAC recently hinted at the
growing importance of Enrollment Scientists…..
To be fair, the application of analytical techniques to solve business
problems within higher ed is not new, and a number of vendors have been
providing these services for some time now. However, we rarely have
“Big Data” in Enrollment Management, but I will talk about that in
future posts. Most of us who analyze Enrollment Management problems
barely have Medium Data. However, what we do have is disparate data.
In this post, I want to discuss the relationship between Data Science
and Enrollment Management. I contend that Enrollment Scientists:
Have both educational and practical experiences dealing with
Are capable with one or more programming languages that are executed
on the command line. I will come back to this in a moment. If you
prefer Linux environments to Windows, you are well on your way to
being an Enrollment Scientist. The key takeaway is that dirty data
does not scare you, and you rarely believe that “manual” data
cleaning is the correct approach. If your data operations staff are
cleaning things manually, there is a good chance your process can be
When it comes to discussing advanced methodologies and the results
from these approaches, Enrollment Scientists are able to synthesize
the key facts to a non-technical audience. I believe this skill to
be the hardest of them all.
To put it simply, I believe that Enrollment Scientists are capable of
understanding the strategic reasoning behind a problem, can hack
together datasets that come in a variety of shapes and sizes, and
are able to effectively communicate the “so what” behind the results to
senior leadership. Basically, Enrollment Scientists are the intersection
of IT, Analytics, and Management.
Technical Tools of the Trade
As referenced above, predictive techniques are just one tool in the
Enrollment Scientist’s toolbox. The items below are the skills that I
believe are critical to the success of an Enrollment Scientist.
Basic understanding of the command line (free): Often times the
robust open-source tools require execution of a few basic commands
in a terminal. Trust me, it’s not that scary. Do you point and
click your way to do the same task all of the time? More than
likely, you can write some basic code to automate that boring task!
All operating systems provide users access to the command line.
R andPython (free): Currently there is a pretty heated
debate on “Python versus R”, but I believe that successful analysts
today need to have facility with both languages. Each have their
strengths. I am significantly better in R, but can usually hack
something together in Python if need be. Admittedly there are times
when R begins to choke, but I rarely have to write rock solid
production code; simply, I just need a reproducible answer .
Worth noting, both R and python are being rapidly developed and are
used at all of the major tech companies today, including Facebook,
Google, Twitter, and Spotify, just to name a few. Did I mention
that both are free? Yes, free…. SAS and SPSS are expensive and
irrelevant, and you can quote me on that……
Tableau (relatively speaking, cheap as hell): In 2014, Gartner
the leader in BI software. The BI tool is an amazing resource for
exploring data. While it is not intended to clean data, you can
often getaway with some lightweight data manipulation. Tableau’s
real strength is allowing you to rapidly explore your cleaned
datasets. I often use Tableau to look at the data that I push to
MySQL after collecting/cleaning in R.
SQL (free): Enrollment Scientists will inevitably be asked to
extract a variety of datasets from Relational Databases (think: your
SIS, or Banner). Knowing some basic SQL can really cut down on the
turnaround time to answer key strategic questions.
Relational Database Engines (free): Today’s Enrollment
Scientists need to have a basic understanding of the variety of ways
to store data within databases. I have MySQL installed on all of my
machines; it’s simple and just works. Both R and Python provide
interfaces to MySQL, as well as a included connection to SQLite. We
can always store data in text files, but sometimes it becomes easier
to hack together a pipeline where you store your cleaned data into a
RDBMS. Tableau, listed above, has an amazing array of drivers to
access the datasets that you build in your RDBMS of choice. Both
MySQL and SQLite are free.
NoSQL Databases (free): Contemporary datasets are not
rectangular in nature. If you play around with Twitter’s API, you
will notice that the data feed is returned in JSON format. In R,
you can map JSON to a list of lists, and in python, to a dict. The
point being that getting raw data in a row by column format is a
luxury. My two favorite tools at the moment are MongoDB, which
nicely stores JSON, and Neo4J, which effectively handles graph data.
Both MongoDB and Neo4j have bindings to R and Python, so we can
work with the raw data just like we would in SQL. Both MongoDB and
Neo4j are free.
Machine Learning Techniques (free): Machine learning means a
wide array of things these days, but to me, it’s having a good
understanding which advanced technique can (and should) be applied
to your current research problem. This means having a firm
understanding of when to use regression, clustering, and data
reduction techniques. It’s also worth noting that some of the
“hotter” techniques like sentiment analysis, graph analysis, and
recommendation engines have a number of applications in Enrollment
Management. In future posts, I will discuss how to use these
techniques. In the short run, here is a previous
where I use a very simple toy dataset to highlight how you can
leverage data to predict student yield.
Markdown (free): Markdown is a markup language that is not only
easy to read and write, but can be converted to a number of output
formats, including HTML and Word. In R, it’s possible to automate
your entire workflow using Markdown. You can clean data, generate
slide decks, journal articles, and even interactive websites when
you use RMarkdown. So what? Your productivity skyrockets when you
can build an HTML report and have it emailed to you every morning
before you get into work. Trust me, it’s amazing.
Github (free-ish): Github is a cloud-based solution to store
your Git (version control) projects online. Git makes it possible
for your to lock in versions of your code and analysis, even
reports. No more saving your files with v1, v2, etc. It’s the
bee’s knees but does take some getting used to. The learning curve
is worth the effort.
Outside of Tableau, all of the skills and software listed above are
free, as in, free beer. Even Tableau has Tableau Public, but there are
usage limitations, Nonetheless, everything you need to be effective
within Enrollment Science is (basically) free. Of course, it takes time
to hone these skills, many of which can not be learned from a text book,
but the efforts will pay off in spades. Enrollment Science is no longer
a luxury, but a necessity in order to compete in today’s rapidly
changing higher education landscape. From here, I aim to highlight how
easy it is to get started within Enrollment Science. I am very
passionate about this topic, so I hope to share that passion with you.
My aim is to introduce you to the tools listed above, and apply
advanced techniques to datasets that you already have in-house. Today,
Enrollment Management divisions are asking increasingly complex
questions. Luckily, industry (i.e. for profit companies) have proven
that we can leverage data to improve our position in the market, so it’s
about time we get better at data within Enrollment Management.