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Faculty

EECS Department Faculty: Howard A. Blair, Tomislav Bujanovic, Ilyas Cicekli, Nihan Cicekli, Stephen J. Chapin, Biao Chen, C.Y. Roger Chen, Shiu-Kai Chin, Jun Hwan (Brandon) Choi, Wenliang (Kevin) Du, Sara Eftekharnejad, Ehat Ercanli, Makan Fardad, James W. Fawcett, Prasanta Ghosh, Jennifer Graham, Mustafa Cenk Gursoy, Can Isik, Mina Jung Mehmet Kaya, Andrew Chung-Yeung Lee, Jay Kyoon Lee, Duane L. Marcy, Patrick McSweeney, WonKyung Park McSweeney, Chilukuri K. Mohan, Jae C. Oh, Susan Older, Vir Phoha, Qinru Qiu, James S. Royer, Tapan K. Sarkar, Q. Wang Song, Sucheta Soundarajan, Jian Tang, Yuzhe (Richard) Tang, William C. Tetley, Pramod K. Varshney, Senem Velipasalar, Li Wang, Yanzhi Wang, Edmund Yu, Reza Zafarani

Math Department Faculty: Uday Banerjee, Pinyuen Chen, Dan Coman, J. Theodore Cox, Steven Diaz, Jack E. Graver, Duane Graysay, Philip S. Griffin,Tadeusz Iwaniec, Hyune-Ju Kim, Mark Kleiner, Leonid Kovalev, Loredana Lanzani, Graham J. Leuschke, Adam Lutoborski, Joanna O. Masingila, Terry R. McConnell, Claudia Miller, Jani Onninen, Evgeny Poletsky, Declan Quinn, Minghao Rostami, Lixin Shen, John Ucci, Gregory Verchota, Andrew Vogel, William Volterman, Yi (Grace) Wang, Stephan Wehrli, William Wylie, Yuan Yuan, Dan Zacharia.

Description

The demand for large-scale data analytics is rising rapidly in various areas of the economy, including the critical infrastructure, healthcare, and IT sectors. The M.S. program in Data Science has been designed to prepare graduates with the data science background to meet the growing need. Because data science is a new and rapidly changing field, it requires professionals who have the technical depth to develop new and statistically sound techniques in cases where existing methods fail. These professional also require sufficient mathematical understanding to use and adapt the new methods that emerge in this dynamic field.

The M.S. in Data Science is a 30-credit program that comprises 15 credits of core coursework, 12 credits of data science electives, and 3 credits of a capstone project. The core ensures that all graduates of the program have the necessary skills to perform largescale data analytics. The electives allow students to augment their data science knowledge in ways that meet their individual goals and objectives; some elective courses focus on applications of data science, while others provide technical knowledge that increases their ability to adapt existing data analytic technique to novel big data challenges. Students apply their skills and knowledge, gained throughout the program, to develop and carry out a data science project in the area of their interest (e.g., business, economics, bioinformatics) using real-world data.

Admission

Successful applicants will have completed a B.S. degree with a 3.0 or better grade point average (GPA) and have successfully completed prior coursework in:

• Introduction to programming
• Multivariate calculus
• Elementary statistics (e.g., MAT 222 or CIS 321 or MAT 421)

The course work requirements can be waived for applicants who demonstrate equivalent knowledge obtained through work or other experience. The admissions committee evaluates the overall academic record of an applicant and uses the following guidelines (GRE scores refer to the New GRE Score System):

• GRE Verbal score of 150 or better
• GRE Quantitative score of 155 or better
• GRE Analytical Writing score of 3.5 or better
• For international students, a TOEFL computer-based score of 223 (Internet-based score 85; paper-based score 563) or better.

Financial Support

Some, but not all, students may receive merit-based tuition scholarships.

Degree Awarded

Master of Science in Data Science - The M.S. in Data Science is offered both residentially and online. On-campus courses are delivered through the traditional semester format: students take courses in the fall and spring semesters; some courses may also be offered during the summer. Online courses are delivered with four starts a year: courses run 11 weeks, with the required contact hours achieved through a mix of asynchronous and synchronous course interaction.

All students must complete 30 credits of coursework, comprising 15 credits of core courses, 12 credits of electives, and a 3-credit capstone course, as described below.