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University of Delaware - Alfred Lerner College of Business & Economics

By Lerner January 24, 2017

The University of Delaware’s Lerner College of Business and Economics will offer a new course MISY631, “Data Mining for Business Analytics,” this spring 2017 semester.

“Data Mining for Business Analytics” is an interdisciplinary course that emphasizes both the technological and business sides of data mining .

“These days, there’s always more data,” an article in  The Atlantic states. “We gather far more of it then we can digest. Nearly every transaction or interaction leaves a data signature that someone somewhere is capturing and storing… The sheer scale of this data has far exceeded human sense-making capabilities. At these scales patterns are often too subtle and relationships too complex or multi-dimensional to observe by simply looking at the data. Data mining is a means of automating this process to detect interpretable patterns; it helps us see the forest without getting lost in the trees.”

Lerner College associate professor of accounting & MIS Xiao Fang, who will lead the course, said that this combination will help graduate students to not only understand technology and data mining, but to use it when making critical business choices.

“Data is so important,” Fang said. “That’s a fact.”

“Business managers have to make so many different kinds of decisions,” he said. “Many MBA students may not be data scientists, but they still need to know data if they want to make data-driven decisions.”

Despite the importance of data, Fang said, many courses only discuss technology in the abstract, as a mysterious “black box.”

“But data mining is different,” he explained, because professionals need a solid understanding of data mining in order to properly utilize it.

The new course will strive to help master’s candidates to build this solid understanding of data mining, Fang said, which will help them to succeed in business while also improving their job prospects and overall skillsets.

“We will talk about the technical side, why and how data mining works,” Fang said. “We will also emphasize the business side, because we need to talk about how to apply these data mining techniques to create a valuable business.”

He describes this course as the first to introduce data science and data mining methods in a rigorous way while also applying data science and data mining methods to address business problems.

The course will also provide hands-on applications and real-world problems for graduate students as key elements of the program.