view looking upward at skyscrapers

City University of New York School of Professional Studies Online Master of Science in Data Science

Why City University of New York? 

The Master of Science in Data Science online program from the City University of New York (CUNY) expects its students to leave the program with a foundational understanding of both the theory and practice of data science. The program, housed in CUNY’s School of Professional Studies and administered by its Department of Computer Sciences, does not require applicants to have an undergraduate degree in any particular field. Still, it does require students to enter the program with skills in statistics and probability, linear algebra, programming, relational databases, and analytical thinking. An undergraduate GPA of at least 3.0 is also expected. No standardized test scores are required.

Applicants can demonstrate they have the prerequisite skills and knowledge through coursework, relevant professional experience, or through their results on a challenge exam. If applicants do not have the required prerequisite skills, but their background makes them a good fit for the program in the eyes of the admissions committee, they can choose to take a “bridge” program of courses at CUNY before enrolling in the Masters. Although taking bridge courses represents an extra expense for the student, the courses are tailor-made for this program and may save the student much frustration while studying.

Program Snapshot

University Name City University of New York
Location New York
State New York Grade B
Name of Degree Master of Science in Data Science 
School or Department Administering Program Computer Science and Information Systems
Credits 30
Cost per credit $855
Tuition for Entire Program $25, 650
Test Requirements None
Minimum GPA 3.00
Prerequisite Courses and Skills Skills in statistics and probability, linear algebra, programming, relational databases, analytical thinking – skills assessed through coursework, professional experience or a challenge exam
Campus Visit Required? No
Typical Time to Complete 2 Years
When Can Students Start the Program? Fall, Spring
Program Concentrations None
Synchronous classes No
Required Courses
  • Advanced Programming Techniques
  • Fundamentals of Computational Mathematics
  • Statistics and Probability for  Analytics
  • Data Acquisition and Management
  • Data Knowledge and Visual Analytics
  • Business Analytics and Mining
  • Analytics Master’s Research Project
Does the Program Include a Capstone? Yes
Does the program include a practicum or internship? No
Other Features that Make the Program Unique Students create a portfolio,  bridge courses in programming, statistics, and data science math, independent study course option
Program Objectives (quoted directly from the program)
  1. Data Acquisition, Management, and Programming: use industry-standard data science and analytics packages to collect, describe, clean, format, model, explore and verify structured data, unstructured data and big data
  2. Foundational Math and Statistics: demonstrate an understanding of linear algebra – differential equations, linear and non-linear programming (NLP), algorithmic search methods for optimization, integer programming (IP) – probability, Bayesian statistics, univariate and multivariate calculus
  3. Modeling: Use statistical and machine learning modeling techniques to design, build and test/assess models
  4. Model Implementation and Deployment: Implement models for the various descriptive, predictive and prescriptive modeling
  5. Dissemination: Develop/write/present reports to explain/present their models, results, and analyses in plain and easy-to-understand language
Program Description The program covers skills in data acquisition, management and programming, foundational math and statistics, statistical and machine learning models design, implementation and deployment, and dissemination of results through visual and written communication. The program consists of 30 required credits and includes a required capstone research project. 

What will I Learn?

The learning objectives of the program are rigorous and specific, encompassing the core competencies of data acquisition, management and programming; Foundational Math and Statistics; Modeling and Model Implementation and Deployment, and Dissemination. The program emphasizes research skills through a required Master’ s research project. The curriculum is more technical than others, with a required course in Fundamentals of Computational Mathematics. Students are also prepared for the data science job market by creating a portfolio of their work to demonstrate skills to employers and can choose to delve more deeply into a personal interest through an elective independent study course. 

In addition to the capstone and computational mathematics courses, students are also required to take Statistics and Probability for Analytics and Advanced Programming Techniques. Data Acquisition and Management, Data Knowledge and Visual Analytics, and Business Analytics and Mining round out the suite of required courses. All other courses are elective, and there are no program concentrations.

How much will the program cost?

Data Science master’s students at CUNY SPS are required to complete a lower-than-average 30 total credits. At the cost of $855 per credit, this brings the total cost of the degree to $25,650 for out-of-state students. The CUNY system is committed to accessible education and serves a diverse student body, offering a steep discount to New York State residents, even for students in online programs. 

How does the program fit into my life?

The program is 100% online, with no required visits to campus. Students can enter in Fall or Spring semesters and may choose to take bridge courses before enrollment.  


If you live in the Empire State, this program is likely to be attractively priced, but even for out-of-state students, it has compelling features. Students with strong backgrounds in math and statistics, as well as those who would enjoy independent study and building a portfolio, may want to take a closer look.  The technical emphasis of the program may be less welcoming to students with a less quantitative background.