The University of Missouri’s unique Data Science and Analytics master’s degree program boasts concentration areas in geospatial analysis, biotechnology, high-performance computing, data journalism/strategic communication and human-centered design for data. These areas are tailor-made for professionals looking to expand their horizons or companies looking to stay on the cutting edge of data science.
The curriculum and resources for each focus area are built off of world-class research from the program’s faculty, and the human-centered design for data track hews closely to Program Director Sean Goggins’ research.
Goggins, an associate professor of the MU Electrical Engineering and Computer Science Department and the MU Informatics Institute, has spent his career studying the intersection between human behavior and computing, funded in large part by the National Science Foundation.
He knows the difficulties inherent in studying topics in this realm as well as anyone, and his goal with this specific degree track is to help social scientists — such as sociologists, anthropologists, psychologists and more — understand computing well enough to draw valid conclusions.
“When you think about how we use Facebook or Twitter or open-source software, my research focuses on how human behavior is affected by technology,” he said. “If I’m going to do that well, there has to be this exchange between social science and computing. Both people have to be at that table.
“The data you’re looking at is possibly large volumes of online log, trace and text data. All the things people say. Social scientists want to answer social science questions about phenomena occurring through technology. But they don’t know much about programming.”
Extracting the data and managing large chunks of data in a way that allows for statistically-significant conclusions to be drawn is a massive hurdle for many in the social sciences. In many cases, gathering that data requires a baseline knowledge of one or more programming languages, such as Python or R.
“What I really want to do in both cases is take away this giant barrier of managing an environment, of having to know a whole bunch of things about what programming languages do and get to a point where they can ask research questions,” Goggins explained.
Goggins has been working toward solving that dilemma for most of the last five years, publishing multiple papers as well as two books: Computer Supported Collaborative Learning in the Workplace with Isa Jahnke and Building Big Data Factories with Nicolas Jullienn and Matei Sorin. To that end, he and a team of researchers from Wikimedia and the University of California, Berkeley used Jupyterhub, an open-source application that allows for the sharing of live code. The team built notebooks with applicable code that allows social scientists to extract and process large datasets with just a few minor tweaks to the code, allowing them to quickly swap out variables rather than having to start from scratch.