Data Has Never Been More Powerful

Chart Like Minard
Big Data Visualization

Data is all around us. In fact, there are now trillions of gigabytes of data, especially in health care, the fastest growing industry that now includes a massive amount of COVID-19 data.

As statistics continue to proliferate and envelope us daily, we are flooded with an onslaught of charts, graphs, diagrams, and multimedia extravaganzas vying for our attention. But, the voluminous amount of data begs the question: How do we interpret data for informed decision making?

A new interdisciplinary course for business, computer science, and technology management students, Machine Learning and Artificial Intelligence for Business, will be offered at the University of Bridgeport beginning in Fall 2020. The course was designed to help future business leaders, including business analysts and project managers make informed decisions in a world where data generation is skyrocketing.

Elif Kongar, professor of technology management and mechanical engineering and chair of the Technology Management Department in the School of Engineering, said that this course will provide current and future business decision makers with deep insights into state-of-the-art analytical frameworks and methodology selection. The course also will be integrated into the department’s core MS curriculum, complementing the concentration in Information Technology and Big Data.

Students will learn about machine learning algorithms, how AI systems are created, and ways to evaluate the pros and cons of coding programs.

Kongar explained, “Students will learn about Python and R codes and snippets of things, but the primary goal is understanding machine learning and artificial intelligence, which algorithms to use under what circumstances, as well as the dominant applications being used in industry today.”

Students will tackle real-world problems in this course. “They might know how to conduct a regression analysis using R, but I want them to learn what purposes it serves, how it may evolve over time, and to understand the big picture,” Kongar said.

To grasp the concept of machine learning, Kongar references pioneer Arthur Samuel who, in 1959, called it “a field of study that gives computers the ability to learn without being explicitly programmed.”

Fast forward to Carnegie Mellon University Professor Tom Mitchell’s 1997 definition of machine learning:  A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

In short, machine learning, a subset of AI, is the study of computer algorithms that improve automatically through experience. However, machine learning is not a new concept. According to Kongar, “Autopilots were installed on airplanes in 1914. What has changed since then is instrument processing capability.”

Essential for today’s effective business leaders is an understanding of how best to communicate the data findings to supervisors, project collaborators and decision-makers across an organization.

Making data understandable and visually appealing is also not new. In 1869, Charles Joseph Minard, cartographer extraordinaire, drew a chart of Napoleon’s 1812 march to Russia. Renowned American statistician Edward Tufte has said that the chart “may well be the best statistical graphic ever drawn.” Minard ingeniously mapped multiple data subsets of the disastrous journey of Napoleon’s army in one visualization, including temperature, time scales, and other factors.

In this course, students will also learn to hone their communication skills, which is pivotal to career advancement. Kongar cites famous AI scientist Amit Ray to underscore this point, “As more and more artificial intelligence is entering into the world, more and more emotional intelligence must enter into leadership.”