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Aug 04, 2025
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2025-2026 Graduate Catalog
Applied Data Science, MS
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Contact
Jeff Saltz
Website
https://ischool.syracuse.edu/academics/applied-data-science-masters-degree/
Description
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 Master’s Applied Data Science | Syracuse University Online
Admission
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/
Facilities
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
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Student Learning Outcomes
Successful students in the Master’s of Applied Data Science program will be able to: - Collect, store, and access data by identifying and leveraging applicable technologies
- Create actionable insight across a range of contexts (e.g. societal, business, political), using data and the full data science life cycle
- Apply visualization and predictive models to help generate actionable insight
- Use programming languages such as R and Python to support the generation of actionable insight
- Communicate insights gained via visualization and analytics to a broad range of audiences (including project sponsors and technical team leads)
- Apply ethics in the development, use and evaluation of data and predictive models (e.g., fairness, bias, transparency, privacy)
Required Core: 15 credits
The 15-credit required 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. Concentration: 6 credits
Concentrations allow students to select course work that matches their professional interests and planned career paths. Students are required to select one concentration below and complete two classes, or 6 credits from that concentration. AI
Know how to use advanced deep learning predictive models in an ethical way Big Data
Know how to use advanced applications, tools and packages to work with very large datasets Data and Business Analytics
Understand how predictive analytics can be leveraged across a variety of business contexts to generate business insight. Data Pipelines and Platforms
Improve your data engineering skills by practicing collecting data, parsing/muging the data, doing feature engineering as well as storing the data in an appropriate data repository Language Analytics
Understand how to work with unstructured textural data, via both text mining (looking for patterns in the words) and natural language processing (parsing and analyzing the text from a linguistics perspective) Project Management
Know how to best manage and deliver useful, ethical, and actionable insight and tools. Visual Analytics
Use visualization and interaction to generate insight from data and their associated predictive Electives: 12 credits
Students can also choose any course from a different concentration for three credits, an additional course in the chosen concentration if available, or any course from the list below. Exit Requirement: 1 credit
Students take IST782 in their last semester or term of study. 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. Notes
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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