Valparaiso University: Helping Forge the Future of Big Data and Analytics

September 28, 2018 1 comment

Businesses and organizations deal with huge amounts of data every day, but given the complexity in data, it becomes difficult to make sense of it all. As a result, turning information into valuable insights has become increasingly crucial as companies strive to stay ahead of the competition. The Analytics and Data Science Programs at Valparaiso University (Valpo) is training the next generation of data scientists offering a cutting-edge academic program to analyze and solve real-world data problems faced by industries globally.

Valpo’s programs have two features that make it stand out from its competitors, which are intimately tied together. First, students begin working with external clients and real, decision-level data as early as their second semester (that is, freshman year). Second, Valpo has extensive ties with non-profits and government agencies, due to its religious affiliations and history of social impact (1st in the Nation for Contribution to Public Good by the Washington Monthly). Together this means that every year a student studies at Valpo, their classwork and time can contribute to changing the world into a better place.

 

The Distinguished Analytics Leader

Dr. Karl Schmitt is the Director of Data Sciences at Valparaiso University (Valpo) in Indiana. He’s run the Masters in Analytics and Modeling program since 2014 and is the founding director of Valpo’s Bachelor’s in Science in Data Science. He recently received a TRIPODS+EDU grant from the National Science Foundation (NSF) to investigate student difficulties in learning data science. Valpo is a prime site for doing this sort of education-focused research. He hopes to continue to expand that work in the near future.

Dr. Schmitt took over directing the Analytics and Modeling (AMOD) program in 2014, one year after he was originally hired at Valpo. “For me, this was incredibly exciting as the ideas central to the program, applied mathematics with simulation, modeling, and statistical analysis were the core of my scientific background and research areas,” Dr. Schmitt said. Since then he has taken a lead role in shaping both the existing graduate program (AMOD) and forming a new undergraduate major, Data Science. Dr. Schmitt’s primary role within both programs is to help shape the student experience to develop analytical talent from recruitment to employment. “I help with a variety of recruitment activities, advise all students enrolled in the programs, teach several of the key courses, and help many find internships or jobs after graduation”.

Under Dr. Schmitt’s leadership Valparaiso University established a full undergraduate major in Data Science and developed two new undergraduate courses (for Valpo) with two more coming next year. The enrolment in the graduate program (AMOD) has also increased by over 300%. “We have also received a National Science Foundation grant for education research through the TRIPODS+X mechanism, one of only 4 educational grants,” he added.

 

Challenges Faced by the Industry

Dr. Schmitt believes the biggest challenges faced by the broader field and industry is fragmentation and lack of vision across disciplinary boundaries. Turf wars between mathematics, statistics, computer science, business, or whoever, aren’t really going to serve the further development of the field. Moreover, he thinks lack of willingness to see the value of other’s approaches to issues can blind researchers and practitioners to major issues. Central among these is lack of mathematical/statistical support for conclusions, reproducibility of results, and inherent bias in any data or analysis.

 

AI and Analytics to Drive Disruptive Innovation

Commenting on disruptive technologies, Dr. Schmitt said, “Just as previous manufacturing technologies have impacted the type of work that the average employee must do, I expect that Artificial Intelligence (AI) and Analytics will also drive major innovations in work-flow and process. Previously we saw first factory line workers improving the throughput, and often quality, of hand-made goods. Later improvements in machine automation moved workers into supervisory and troubleshooting roles of the machine processes. I think there will now be another level of abstraction/distance between the produced good(s) and the oversight workers with AI”.

 

On Advice to Aspiring Data Science Professionals

“Learn Python. Seriously, I’m passionate that Python will over time become the core language for data science and analytics. Mostly because it’s simply got a far, far wider user-base than other languages (maybe not in this specific field, but overall)”.

This is followed closely by GIT – and not just the desktop version!

“I keep hearing about companies from nearly every sector wanting analytics skills, but the challenge is that each segment requires very different training to be on the edge of trends. Much of my focus is on identifying, and then providing to students the skills that will allow them to ride today and tomorrow’s trends with equal skill.”

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