The Big Data Analytics, AI and Robotics industries are undergoing multiple disruptions in its functioning and the trends will accelerate in the future. Emerging technologies such as deep learning, machine learning, computer vision and robotic process automation (RPA) have significant potential to streamline industries. Barrett W. Thomas, Head – Department of Management Sciences at The University of Iowa’s Tippie College of Business shares how training students at all levels in the analytics life cycle will make the future even better and pave the way for new innovations in the industry.
Please share your thoughts on the growth of Big Data Analytics and AI. How do you see these technologies impact the business sector?
These technologies are here to stay. The question is how quickly and in what ways they will spread from the Googles of the world to small- and medium-sized businesses.
How is Tippie College of Business, The University of Iowa’s Analytics and Data Science Program contributing to the growth and transformation of analytics and big data education?
We are training students at all levels (undergraduate, Master’s, Ph.D.) to execute at every stage of the analytics lifecycle, from gathering and storing the data to visualizing the data to predicting the future to making the future better. We also emphasize students’ ability to communicate their solutions. However advanced one’s analytics, there is no insight if you cannot explain it to others.
What is the unique feature of Tippie College of Business, The University of Iowa’s Analytics and Data Science Program?
Tippie’s Department of Management Sciences has had faculty experts in all areas of analytics for years. For us, analytics is not the new things to chase, but the thing that we have always done. As demand for analytics education grew, we built on this strength to design and deliver a world-class curriculum. Importantly, we can do all of this without needing to rely on faculty in other areas of campus as so many programs do.
Kindly brief us about your role at Tippie College of Business, The University of Iowa’s Analytics and Data Science Program and your journey in this highly promising sector.
As the head of the Department of Management Sciences, it is my job to work with the faculty directors and staff who run our programs to make sure that we are meeting the needs of our students and the employers who hire them. I found my way to analytics via an internship at third-party logistics firm while being an undergraduate math major. The internship required creating a database and manipulating that data that was eventually used as input into an optimization model. This work led me to a Ph.D. in Industrial and Operations Engineering at the University of Michigan. I have been working in analytics ever since.
What would you advice to aspiring big data and analytics candidates?
Learning analytics in the classroom is only part of the journey. You need to learn to ask the right questions to frame a problem for analysis. We hope to develop these skills through our required capstone courses, but we also hope that students practice in open challenges, work with non-profits, and work in meaningful internships.
Please share some major achievements of Tippie College of Business, The University of Iowa’s Analytics and Data Science Program under your leadership.
In only five years, our undergraduate major has grown to be the second largest in the Tippie College of Business. This growth demonstrates both the quality of the program and the success of its graduates. Similarly, our Master of Business Analytics has grown to over 300 students, taught both on-campus and in satellite locations around the state. We are proud of our ability to have scaled a quality curriculum to meet the needs of the employers of the state.
Could you please tell us about the latest employment trends in big data and analytics industry?
The data suggests that there is a significant shortage of job seekers with the analytics skills employers need. Yet, it is also important to note that these skills are continuously evolving. The technologies that were state-of-the-art when we started our programs only five years ago are now stale. Knowing R and basic prediction algorithms are no longer enough. Now, students must be adept in lower-level programming languages, random forests, and deep learning. We have also seen an emphasis on communications skills. Students can no longer get by simply by being good with numbers. They now must communicate their solutions.