2022-2023 Graduate Course Catalog 
    Jul 23, 2024  
2022-2023 Graduate Course Catalog [ARCHIVED CATALOG]

Applied Data Science, MS

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Jeff Saltz
Program Director
(315) 443-2911




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 about 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. The application checklist can be found here: https://ischool.syr.edu/admissions/checklists/applied-data-science-checklist/


Classrooms and computer labs 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. Collect, store, and access data by identifying and leveraging applicable technologies
  2. Create actionable insight across a range of contexts (e.g. societal, business, political), using data and the full data science life cycle
  3. Apply visualization and predictive models to help generate actionable insight
  4. Use programming languages such as R and Python to support the generation of actionable insight
  5. Communicate insights gained via visualization and analytics to a broad range of audiences (including project sponsors and technical team leads
  6. Apply ethics in the development, use and evaluation of data and predictive models (e.g., fairness, bias, transparency, privacy)

Program Requirements

Primary Core: 18 credits

The 18-credit primary core includes foundational knowledge in databases, data analysis and business analytics. Students will complete courses in an order which builds foundational knowledge and skills in preparation for more advanced work.

Secondary Core: 6 credits

Students select 6 credits from one of the tracks below.  

Electives: 9 credits

Students can also choose any course from a different track for three credits, an additional course in the chosen track if available, or any course from the list below.

Exit Requirement: 1 credit

Total Credits Required: 34

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. 

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