Master of Science in Data Science

Format: Online
Application Deadlines
  • Fall 2024 Priority Deadline: April 4, 2024
  • Fall 2024 Regular Deadline: May 16, 2024

The MS in Data Science online degree program helps students earn the credentials and acquire the skills needed to enter or advance in the fast-growing field of data science. Ranked by Fortune magazine as one of the best online master's programs in the nation, the MS in Data Science offers foundational knowledge and hands-on programming competencies, resulting in project-based work samples that are in great market demand.

The program’s learning objectives and demanding, hands-on courses are designed around employer needs. Throughout their time in the program, students build portfolios of increasingly complex projects using popular programming languages such as R and Python, which mirror the current experience and demands of the workplace. Students build predictive and prescriptive models, practice giving presentations, and review each other’s work in a convenient online setting, ensuring that they are equipped with the expertise most valued in today’s marketplace. The MS in Data Science program culminates with a capstone project that represents highly sophisticated, but practical, solutions to address real world problems.

The program covers a broad range of disciplines:  

  • Probability and statistics: descriptive statistics, sampling techniques, discrete probability models, sampling, statistical distributions, correlation, and null hypothesis testing 
  • Applied math: matrix manipulation, linear equations, and loss functions 
  • Programming: creating, testing and optimizing regression, classification, and Bayesian models using R and Python 
  • Data acquisition and management: acquiring/importing data frames; cleaning, normalizing, and manipulating data via SQL 
  • Critical thinking and communication: framing real-world phenomena as quantitative representations and explaining results in simple, clear, and cogent language 

Additionally, the program’s faculty comprises committed and engaged technology practitioners who are experts in their fields.  They invest time in building courses on the use of open source best-practice tools that satisfy high employer demands for quality programming and the use of advanced techniques.

Career Prospects

The MS in Data Science program prepares graduates for a variety of technical and managerial positions in such fields as machine learning, artificial intelligence, business intelligence, data governance, and market research.

Admissions Criteria

Applicants must possess a bachelor’s degree from a regionally accredited institution, with a preferred GPA of 3.0 or higher on a 4.0 scale. Applicants are required to write a personal statement, upload a resume, and provide two letters of recommendation. Letters of recommendation may be submitted before or after submitting an application. 

As an interdisciplinary field, we welcome applicants from diverse professional backgrounds. In terms of qualifications, we look for candidates with either strong technical (programming) and quantitative (applied math) backgrounds, or those individuals with a keen interest and ability/willingness to learn.  

Bridge Tutorial

All applicants are required to take the Bridge Tutorial, a free, self-paced, online mini-course to assess applicants’ skills required to succeed in the program.  

The course is designed so that qualified/experienced applicants can quickly/easily demonstrate their competencies by answering a set of multi-choice questions. However, those applicants who don’t pass on the first try are given a second chance, receiving feedback on their responses as well as instructional aides and tutorials on the covered subject matter. Given that tutorials on the subject matter are provided, and the assessment is not timed, it’s essentially an “open book” test. 

For questions, please email datascience@sps.cuny.edu.

Application Deadlines

  • Fall 2024 Priority Deadline: April 4, 2024
  • Fall 2024 Regular Deadline: May 16, 2024
Apply Now

Student/Alumni Profiles

Duubar E. Villalobos Jimenez

MS in Data Science 2019

"Since my master’s program was 100% online, I had the experience as to how to cope with online work activity. When COVID hit, and everything became remote, I was able to switch gears seamlessly.”

James Hamski

MS in Data Analytics 2017

"It’s not just about perception, I can “walk the walk” and produce results because of the skills I gained.”

Jonathan Hernandez

MS in Data Science

"The most enjoying aspect of the program is the fact that we get to work on real-world programs and can apply our skills learned in these courses to solve real-world data science problems."

Youqing Xiang

MS in Data Science

"There are four key success factors for a data analyst: computer programming and mathematical skills, domain knowledge, communication, and teamwork. CUNY SPS has prepared me in all of these areas."

Recent News About Master of Science in Data Science

CUNY SPS student working on laptop

MS in Data Science Continues to Grow Its List of Top Honors

February 22, 2024

Tech Guide's ranking is the latest accolade for the CUNY SPS MS in Data Science program, which has been continuously earning high ranks for its academic excellence and value.

Best Online Master’s in Data Science Programs in 2023

February 17, 2023

Fortune Education

The CUNY SPS MS in Data Science program has been ranked #16 on Fortune Education's list of Best Online Master’s in Data Science Programs in 2023.
Intelligent.com 2023 Best Online Degree Programs Badge

CUNY SPS Recognized for 4 Top Programs in the U.S.

December 28, 2022

In a new ranking for 2023, CUNY SPS was named #7 on Intelligent.com's list of Best Bachelor's Degrees and Programs.

Social Activism and Firm Valuation: An Examination of ‘Taking a Knee’ Protests and National Football League Sponsors

November 29, 2022

Review of Pacific Basin Financial Markets and Policies

Arthur O'Connor, academic director of the MS in Data Science and BS in Information Systems programs, co-authored the research study, "Social Activism and Firm Valuation: An Examination of ‘Taking a Knee’ Protests and National Football League Sponsors." The study was published in the Review of Pacific Basin Financial Markets and Policies.