One in a series of articles profiling university programs focusing on big data and analytics education.
Since starting in 1965, Cornell University’s education program for analytics professionals has adapted with the times. Founded as the School of Operations Research and Industrial Engineering, the faculty voted to change the name, replacing “industrial” with “information” in 2007 to better reflect the curriculum—coursework focused on data technologies driving the field—and the demand of the marketplace.
The change was part of an ongoing process at the school known as ORIE. The school added its first class in data mining to the curriculum in 2000, and added a concentration in data analytics to its Master of Engineering program in 2004.
“Probability and statistics have been a part of our curriculum from the beginning,” said Mark Eisner, a former director of the engineering master’s program who now serves as a communications associate. “It’s been very much based on dealing with data, dealing with uncertainty and making decisions, but then also strong emphasis on optimization and simulation technologies. At its core, it’s the use of the scientific method to solve business and other organizational problems.”
The school admitted 90 students for the Master of Engineering in fall 2012, according to Kathryn Caggiano, the program’s current director. Most students complete the program in two semesters, but students who join the program from another field outside of operations research or analytics can take an extra semester to take prerequisites and catch up.
“One of the advantages of a field like this is that it lends itself so well as a complimentary discipline to so many others,” Caggiano said.
She said successful candidates for the program have a sufficient background in mathematics, some computing experience, and have studied statistics. After graduating, she added, students enjoy promising job prospects. Particularly the data analytics concentration, she said, “can be a really powerful combination on the job market.”
The program is a mix of case-based work and traditional academic theory courses, Caggiano said. Students use R, SAS and SPSS statistical languages, and are trained in Microsoft Excel Visual Basic.
Students begin the program with a week-long seminar on communication and teamwork, two crucial parts of problem solving, Caggiano said. They put those “soft skills” to use in a capstone project during the final semester. Teams of students solve business problems using data from an industry partner. Partners include the likes of Procter & Gamble and Walmart.
Students who choose to concentrate in financial engineering also take a third semester in Manhattan. Their capstone project involves working for a financial services firm on Wall Street.
- Master’s program started in 1965, data analytics concentration formed in 2004.
- 90 students in Master of Engineering program.
- Full time program; most students finish in two semesters.
- Tuition for two semesters is $43,185.
- Capstone project class involves students working in teams on a real business problem.
- Students work with R, SAS, SPSS and Microsoft.
- Seven possible concentrations or minors: applied operations research, data analytics, financial engineering, information technology, manufacturing and industrial engineering, strategic operations, systems engineering.