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Data Science and Analytics Program Founded by Dr. Goggins Wins Award

The University of Missouri Data Science and Analytics program received the Outstanding Credit Program Award from the University Professional and Continuing Education Association (UPCEA) during the Central Region Conference in St. Louis.
The Data Science and Analytics Masters Program was conceptualized by  Dr. Sean P. Goggins and Dr. Chi-Ren Shyu in the spring of 2013, following Dr. Goggins work on a similar program at Drexel University and Dr. Shyu’s long standing work in data scientific oriented endeavors, including founding the MU Informatics Institute over a decade ago.
Through support from the Mizzou Advantage fund, Grant Scott joined our leadership team in 2015. Later in 2015, core DSA Faculty from across campus signed on to the effort, including:
  1. Yi Shang : Computer Science, Course Coordinator
  2. Dong Xu : Computer Science
  3. Joshi, Trupti : Computer Science
  4. Tenaja, Harsh : Journalism
  5. Thorson, Esther L. : Strategic Communications, Course Coordinator
  6. David Herzog : Journalism, Course Coordinator
  7. Uhlmann, Jeffrey : Computer Science
  8. Gibson, Twyla G. : School of Information Science and Learning
  9. Technologies
  10. Sanda Erdelez : School of Information Science and Learning
  11. Technologies
  12. Shyu, Chi-Ren : Director, MU Informatics Institute
  13. Joi Moore : School of Information Science and Learning
  14. Technologies, Course Coordinator
  15. Ersoy, Ilker : Biotechnology, Course Coordinator

On the Art of the Bio

Writing a personal bio is difficult because you have to talk about yourself as though you actually think you are all that and a bag of chips. I mean, we all do, right? Still, its a weird task and I do not enjoy it. And these things are more dynamic than you would think because what I do, especially, as an academic, especially, has to be refined for the language of a particular audience. Students, colleagues, funders and family, for example. Here are a couple that I recently put together. Now its a blog post.

If you are looking for more of a press release flavored bio, here are a few choices:

Bio 1: After a decade as a software engineer, Sean decided his calling was in research. He is presently a social computing researcher and professor of computer science at the University of Missouri. He is also a co-director and founder of their Data Science Masters program. Sean’s publications focus on understanding how social technologies influence organizational, small group and community dynamics, typically including analysis of electronic trace data from systems combined with the perspectives of people whose behavior is traced. Group informatics is a methodology and ontology Sean has articulated with the aim of helping build consensus among researchers and developers for how to ethically and systematically make sense of electronic trace data.  Structural fluidity, a construct Sean developed with his collaborators Peppo Valetto and Kelly Blincoe, aims to make sense of structural dynamics in virtual software organizations, and how those dynamics affect performance. Working with Josh Introne, Bryan Semaan and Ingrid Erickson, Sean is elaborating on mechanisms for identifying structural fluidity and organizational dynamics in electronic trace data using the lens of complex systems theory. His other work includes collaborations with Matt Germonprez on the Open Collaboration Data Exchange and Open Source Health metrics projects. He lives in Columbia, MO with his wife Kate, two step daughters and a dog named Huckleberry.

Bio 2: Sean Goggins is just a guy. He writes stuff. He’s selfish, but not as selfish as he used to be. He’s painfully well organized, which means he has detailed lists of all the tasks he’s behind on. Computer Science. Social Computing. Learning Analytics. Learning Sciences. Small Groups. Published. Teaches. Funded. Does not suffer fools well. Eats control freaks for lunch. Pulled his groin on a bike ride last Sunday. Is generally concerned about the state of the world, and has enough self assuredness to think what he does each day could possibly make a difference. So, he’s naive. But not as naive as he used to be. He likes to ride his bicycle. 2 tattoos. Father. Step Father. Husband. Currently avoiding writing an actual bio.


Software Engineering and Data Science

People get excited about data science. Especially managers. Its instinctive. We are surrounded by data, nearly all of it overwhelming. Like the partner we dated through high school, it seems like there is something there, but it just doesn’t ever seem to come together. Data science is the camping trip where we figure each other out in our deluge of data.

When you head down that road, you are overwhelmed initially by 3 factoids. First, There is SO MUCH DATA. Second, the data is SO DISORGANIZED. Third, THERE ARE SO MANY TOOLS! We go down the rabbit hole.

Data scientists are, therefore, the janitors on the scene of a massive sewage leak. In the workshop (tool room). What makes data scientists successful or not: that’s what managers want to know. How do I *know* this person can clean up my sewage leak? There are 2 paths:

  1. The data scientist knows your business domain, and has figured out which tools work for your mess
  2. The data scientist has learned about all the tools; and probably cleaned up other messes in a few, assorted domains.

Conceptually, software engineering is about little more than being systematic about how you approach a project and its lifecycle. The discipline can be applied in application development, infrastructure, data science and food preparation (among a host of domains). Yeah, you can do software engineering on food. If you disagree, come over and try out my digital chicken.

I get to say I am a data scientist today because I have a Ph.D, a bunch of papers, and I have been working in “Big Data” since before somebody invented “Big Data”. Some day, somebody please tell me what “Big Data” is; other than an awkward euphemism that is not helping with the gender gap in computing disciplines.

Getting beyond Ph.D level credibility requirements requires systematic training and a software engineering discipline around data. That’s kind of what I do with my projects, which are spread across a host of GitHub Organizations. Many of our repositories remain private because my teams and I continue to publish on them. If you want a peak, drop me a line. Here’s a list of GitHub Organizations for Data Science work that I operate:


Software engineering. Data science. Together. That’s kind of a thing I do. Kind of one of the ways I maintain such a long list of projects.