2019-2020 Graduate Course Catalog 
    Oct 01, 2023  
2019-2020 Graduate Course Catalog [ARCHIVED CATALOG]

Applied Data Science, MS

Return to {$returnto_text} Return to: Academic Offerings


Murali Venkatsh
Program Director
School of Information Studies
210 Hinds Hall
(315) 443-2911

Arthur Thomas
Associate Dean for Academic Affairs
School of Information Studies
110A Hinds Hall

Don Harter
Associate Dean, Masters Programs
Whitman School of Management
Graduate Programs, Room 315


iSchool MS in Applied Data Science


School of Information Studies Faculty: Bei Yu, Yang Wang, Jeff Saltz, Joon Park, Jeff Hemsley, Nancy McCracken, Lu Xiao, Michael Fudge, Martha Garcia-Murillo, Gary Krudys

Whitman School of Management Faculty: Donald E. Harter, Anna Chernobai, Dinesh Gauri, Thomas Barkley, Mary Ann Monforte, John Park, Lai Xu, Reja Velu


Offered jointly by the School of Information Studies and the Martin J. Whitman School of Management, the Master of Applied Data Science degree program is designed to be a professional program of study, with a strong emphasis on the applications of data science to enterprise operations and processes, particularly in the areas of data capture, management, analysis and communication for decision making. We also offer our MS in Applied Data Science online. Learn more at iSchool@Syracuse Online


All candidates should have a bachelor’s degree or equivalent. In addition, it is recommended that potential students have a strong background in a data-intensive domain such as business, science, statistics, research, or information technology. The online program may be of particular interest to early- or mid-career professionals who cannot, or prefer not to, relocate. Applicants should have an interest in interdisciplinary work focused on managing large data sets using information technologies as tools to enable solutions for such organizations as business and public enterprises. Prospective students who have an interest in data science, but lack the recommended undergraduate background, are encouraged to inquire. Individual consultations are available for such prospective students to explore their potential candidacy. Review the applied data science online application checklist.

Financial Support

Merit scholarships are available for the on campus program.


Current classrooms, computer labs and laptop carts within the School of Information Studies and the Whitman School are available for this program; Online facilities provide complete coverage of all required course activities.

Degree Awarded

MS in Applied Data Science

Student Learning Outcomes

Successful students in the Master’s of Applied Data Science program will be able to:

1. Describe a broad overview of the major practice areas in this science

2. Collect and organize data

3. Identify patterns in data via visualization, statistical analysis, and data mining

4. Develop alternative strategies based on the data

5. Develop a plan of action to implement the business decisions derived from the analyses

6. Demonstrate communication skills regarding data and its analysis for managers, IT professionals, programmers, statisticians, and other relevant professionals in their organization

7. Synthesize the ethical dimensions of data science practice (e.g., privacy)

Program Requirements

Common Core

The Common Core (18 credits) includes foundational knowledge in databases, data analysis and business analytics. Students will complete Common Core courses in an order which builds foundational knowledge and skills in preparation for more advanced work.

Applications Analytics Core

The Applications Analytics Core (3-6 credits*) provides an opportunity for the student to choose one or two functional area specializations in accounting analytics, financial analytics, marketing analytics, and supply chain analytics. Students will complete one or two chosen Application Analytics Core course(s) as a way to develop deeper exploration of particular application area(s) for data science techniques.


The Electives (12-15 credits) include coursework in linear models, time series, cloud management, scripting for data analysis, natural language processing, information visualization, data warehouse, text mining, advanced database management and information policy.

Portfolio Requirement

Students will also complete a Portfolio to provide an assessment of learning for their program. Students will choose assignments and projects worked on in courses during the course of study which reflect abilities specified in the program learning outcomes for inclusion in their personal portfolio. A panel of faculty who teach the courses included in the program will review the portfolios of graduates during the students’ final term. The panel will approve the portfolio for each student as a transcript milestone required for the degree.

Total Credits Required: 36

Transfer Credits

6 credits in related coursework can be transferred from other universities with the approval of the Program Director.

Part-Time Study

U.S. citizens, and non-citizens with the appropriate visa and/or immigration permissions for part-time study, may pursue this program on a part-time basis.

Satisfactory Progress

Students are required to have a 3.0 grade point average or higher to maintain satisfactory progress.


On-campus courses are delivered through the traditional semester format in which students take courses in the fall and spring semester, with optional internships in the summer. Section sizes for on-campus classes range from 20-45 students. Online courses are delivered with four (4) starts per year, where courses run for 11 weeks with required contact hours achieved through a mix of asynchronous and synchronous course interaction. Online section sizes run from 12-18 students.

Return to {$returnto_text} Return to: Academic Offerings