IIT Program Blends Business Skills, Ethics with Big Data Training

by   |   April 7, 2014 5:30 am   |   0 Comments

The Exelon Tube enveloping the El train above IIT's McCormick Tribune Campus Center

The Exelon Tube enveloping the El train above IIT’s McCormick Tribune Campus Center

Lulu Kang, assistant professor of Applied Mathematics and Associate Director of the Data Science Program at the Chicago-based Illinois Institute of Technology (IIT), enthuses about the benefits of the school’s Master of Data Science degree program.

“We recognized that there was an urgent need for well-educated talent in data science in this age of big data and that we were poised to take the lead in training and educating the next generation of data scientists,” she said, citing the school’s strengths in high-level applied mathematics, computer science, statistics, and high-performance computation.

IIT’s departments of Applied Mathematics and Computer Science jointly established the Master of Data Science program in June 2013. Students in the program complete courses in statistics, machine learning, data preparation and analysis, data processing and project management.

Related Stories

Oakland U. Prepares Students for Strategic Business Data Demands.
Read the story »

Saint Peter’s U. Unveils Plans for Data Science Master’s Program.
Read the story »

Marketing Analytics Focus of New Program at IIT Stuart School.
Read the story »

Northwestern Offers Online Masters in Predictive Analytics.
Read the story »

But IIT is a school with extensive experience in master’s-level professional education in science and mathematics, Kang said, and isn’t content to send data science grads into the world without the ability to connect skills with ideas. So the rigorous, multidisciplinary program also emphasizes the practical communication, business skills, and ethics necessary for modern technology workers. Graduates are expected not only to understand data sets, but also to have insights, envision results and articulate their findings to non-technical stakeholders. In addition, students are required to attend a one-credit-hour symposium where guest speakers discuss the latest developments in data science.

“This is a professional master’s program,” Kang said. “Our primary goal is to prepare the students for industrial and corporate jobs in data science.”

To that end, Kang said, students are required to take a six-credit-hour summer Data Science Practicum, in which they work with industrial partners to solve problems for business and industry using corporate or industrial data sets. Faculty and industrial partners together evaluate students’ projects.

The program’s industrial partners range from small start-ups to large national corporations. Students also have the opportunity to work with government, academia, or non-profit organizations.

For student Xiaoyu Qian, the choice to enroll in the program was based on the growing demand for data scientists.

“Big data is very hot and I think it has a promising future,” Xiaoyu said, adding, “My undergraduate major was mathematics, so it was very helpful for me. The most useful thing is that it really helps to solve problems with huge data sets, which is required now for many big companies.”

“Educating data scientists is a multifaceted task,” said Shlomo Argamon, professor of computer science and director of the program. “We will teach our students the theory and practice of statistics, data mining, and scalable computing in depth; train them in communications and design skills; and provide them with practical experience integrating their knowledge, guided by academic and industrial mentors.”

Program details

• 12-month full-time program
• 18- or 24-month part-time program
• Some classes available online
• Degree is 33-34 credit hours at $1,250 per credit hour
• Scholarships may be available to highly qualified students

Susan Madrak is a Philadelphia-based writer.

Tags: , ,

Post a Comment

Your email is never published nor shared. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>