Over the last five years, there has been a 372% increase in demand for data analytics. In response, two local UNO professors have developed a data analytics concentration for the economics program. Is economics the logical point to launch a study in data analytics? These professors feel it is exactly the right spot. Local employers and area experts are clamoring for more data talent. These two gentlemen hope to meet that demand. They were kind enough to answer a few questions about the new concentration.
What’s your elevator pitch?
The MS in Economics with a concentration in Econometrics and Data Analysis is designed to supplement the quantitative skills naturally acquired through the pursuit of an economics degree. The concentration was born out of the observation that many economists work in quantitative analysis for various firms and institutions after graduation.
It is becoming increasingly necessary for analysts to understand basic programming, as well as understand how to work with very large, and often unwieldy, data sources in various formats. It is not typical for programming or data wrangling to be taught in an economics program, even though these skills are complementary to the skills learned in the program. We decided that we could strengthen the training of our students by offering what we considered to be the “complete package” in one degree program.
We hope that our students will be fully prepared to do modern data analysis on large data sets upon graduation. Further, their analysis will be grounded in the principles of economics and economic theories about how firms, governments, and individuals behave.
“We hope that our students will be fully prepared to do modern data analysis on large data sets.”
What got you started?
We are definitely not the first to suggest a concentration in data analytics within the UNO community. For at least the last couple of years, the administration has known that analytics was a critical component of industry and, as a university, we were not providing the training that many of our students needed. The fact that Dusty’s position exists reflects this recognition by the administration.
However, we’ve had other indicators within the economics department itself. Many of our MS/MA graduates have ended up with data analytics jobs. It isn’t that we’ve been training them in these skills, but econ students are inherently skilled at quantitative analysis due to the topics covered in an economics program. Many employers have such great need of these skills that they are willing to take a chance on someone that has the capacity to learn these techniques, even if they don’t possess them currently.
Recently, two thesis students took it a step further: determined to research a topic that required analytics skills, they cobbled together a collection of classes, books, and online videos that trained them in the necessary techniques so that they could pursue their research. In many ways, they were our first data analysis (DA) students — in fact, one of them now teaches the MBA DA class when Dusty is unavailable.
After seeing all of these indicators, we began to meet with partners in the community. Regardless of firm size, those we met with all agreed that DA was a major need within their firms.
Why UNO Economics?
There are two major reasons that economics is the logical place to house a data analytics concentration. First, the problem faced by the modern company isn’t lack of data but difficulty finding the important data hidden inside. While many statistics programs can train a student in the techniques necessary to examine vast datasets, they spend much less time discussing ways to generate actionable hypotheses based on that data. This can be very dangerous in the business world. Without proper training, one might conclude that sales (for instance) are driven by an unrelated factor that is simply correlated with some other driving factor. Having a strong theoretical business foundation, even when your primary role is quantitative analysis, provides insight into cause and effect. This improves decision making.
Second, economists are accustomed to working with very dirty datasets. For instance, both of us work with government data in some of our research. This data is messy in a number of ways. First, the government often restricts some data to prevent identification of individuals or firms. Secondly, their survey methods are imperfect, resulting in questionable data. Finally, the datasets are often in forms that must be transformed for analysis. These skills are essential to both broad economic studies as well as data analysis and are already taught within economics programs like ours. This makes it natural to train data analysts within our economics program.
“Having a strong theoretical business foundation, even when your primary role is quantitative analysis, provides insight into cause and effect.”
How did you know what employers needed?
We spoke with numerous firms in the industry including folks from BlueCross BlueShield, Union Pacific and Farm Credit. This is where we really need to thank the folks from Dynamo. They have been amazing and have taken the time to arrange these conversations for us. Additionally, as an IT recruiting firm operating in Omaha, they have an understanding of the data analytics slice of the labor market that we could not see from within academia. This has been immensely helpful in crafting our program.
What surprised you? How did that impact your work?
In our discussions with industry partners, there was a constant theme regardless of firm size: data management and data quality. While some staff members are trained in cutting-edge techniques, it is difficult or impossible to use them when you are still trying to get the data into a usable form. This is how we found our market niche. Our concentration will produce a graduate that understands business, understands analysis, and also understands how to work with and resolve the problems surrounding messy data.
As noted earlier, economists are used to working with imperfect (or even terrible) datasets. Further, this is an environment we could create here within UNO’s College of Business. Our sequence consists of three ‘core’ concentration classes along with electives. All three of these classes will work with dirty data whereby cleaning is an integral part of analysis.
How are you teaching data analytics?
Our program is designed around teaching a student how to program, not how to program in a specific language. Throughout our core, but particularly in the tools course, we teach students how a programming language works. While our courses are in Python (which is unique here at UNO), a graduate should readily be able to pick up another language if necessary. Other languages that should be straightforward to learn later on are Julia, R, or Matlab.
In addition to programming, we’ve also integrated working with databases into our program. Thanks to the phenomenal support from our colleagues in CBA, we have a number of unique databases loaded into a SQL server for our classes. This includes transaction data from our student-run coffee shop, raw Twitter data about fashion, 16 years of NFL data and over 50 years of industry Census of Manufactures data. These datasets are very dirty, but that’s part of the analysis process!
Find out more about the program
The concentration kicks of in January of 2017. To find out more about the UNO economics department or this specific program, visit their web page.