Data Science, MS

Contact

Dr. Jae C. Oh, Professor and Chair

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.

Student Learning Outcomes

Graduates of the MS in Data Science program will be able to do the following:

Systematically collect data to perform a specific task
Store, manage, and represent data in a manner that is amenable to data mining
Describe the nature of the data they have and analyze specific characteristics of the data to develop insight into what the data means
Use techniques such as classification, clustering, and regression to provide statistically valid answers to questions about the inherent nature of the data

Program Requirements

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.

Core Courses (15 credits)

MAT 503 - Matrix Methods for Data Science 3 credit(s) or
MAT 532 - Applied Linear Algebra 3 credit(s)
MAT 523 - Statistical Methods for Data Science 3 credit(s)
CIS 563 - Introduction to Data Science 3 credit(s)
CSE 581 - Introduction to Database Management Systems 3 credit(s)
MAT 695 - Fundamentals of Data Science 3 credit(s)
Note: MAT 503 will be offered online only. Students in the residential program will need to take MAT 532 (Applied Linear Algebra) to satisfy this requirement.

Elective Courses (12 credits)

The Data Science Program Committee will maintain a list of approved Data Science electives. This list will be kept online in a publically viewable location and updated by May 1 each year.

For AY 2017-2018, this list will include at least the following courses:

CIS 675 - Design and Analysis of Algorithms 3 credit(s)
CIS 787 - Analytical Data Mining 3 credit(s)
CSE 682 - Software Engineering 3 credit(s)
CIS 668 - Natural Language Processing 3 credit(s) /IST 664: Natural Language Processing
IST 719 - Information Visualization 3 credit(s)
MAS 777 - Time Series Modeling and Analysis 3 credit(s)

Data Science Capstone (3 Credits)

CIS 669: Data Science Capstone (Fall 2018)

Total Credits Required: 30

Transfer Credits

Up to nine (9) credits may be transferred from other schools, upon evaluation of details by the program coordinator.

Part-Time Study

Part-time study may be permitted, but the program must be completed within four years from the date of admission into the program.

Satisfactory Progress

A GPA of 3.0 must be maintained throughout the program or else matriculation may be terminated.