Master of Science in Data Science

Format: Online
Application Deadlines
  • Spring 2022 Priorty Deadline: November 4, 2021
  • Spring 2022 Regular Deadline: December 9, 2021

The MS in Data Science (previously MS in Data Analytics) 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 last year as one of the Best Value Online Big Data Programs, the MS in Data Science online degree program offers foundational knowledge and hands-on programming competencies, resulting in project-based work samples similar to that of a programming boot camp.

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 IT 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.

Additionally, the program’s faculty comprise 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 use of advanced techniques.

Career Prospects

The MS in Data Science program prepares graduates for a variety of technical and managerial positions, such as data scientist, business intelligence analyst, knowledge engineer, informatics engineer, data analyst, data mining engineer, and data warehousing manager. 

Admissions Criteria

Applicants must possess a bachelor’s degree from an accredited institution, with a 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. An individual interview may be necessary.

As an interdisciplinary field, we welcome applicants from diverse professional backgrounds. However, because the MS in Data Science is a highly quantitative and technical major as compared with MBA-like programs, acceptance requires applicants to demonstrate current skills in:

  1. Statistics and probability including descriptive statistics, skewness/kurtosis, histograms, statistical error, correlation, single variable linear regression analysis, significance testing, probability distributions, and basic probability modeling;
  2. Linear algebra including basic matrix manipulation, dot and cross products, inverse matrices, eigenvalues, representing problems as matrices, and solving small systems of linear equations;
  3. Programming in a high-level language such as Python, Java, JavaScript, C++, C, Ruby, or SAS (2+ years). Applicants must be able to write working code from scratch;
  4. Relational databases including connecting to and manipulating data, working with tables, joins, basic relational algebra, and SQL queries. Two or more years of experience with Microsoft Access can be substituted if the applicant is able to perform the same operations without using Access’s graphical interface; and,
  5. Analytical thinking including the ability to translate real-world phenomena into quantitative representations and, conversely, the ability to interpret quantitative representations with practical explanations.

Skills in these areas will be assessed in two ways:

  1. Completion of credit-bearing coursework with a grade of B or better from an accredited college or university OR 2+ years of relevant experience on a resume; and,
  2. Completion of a mandatory challenge exam that will assess current skill and knowledge in these areas. If you lack the skills required for admission to the program and/or are unable to answer the questions found in the challenge exam, please email datascience@sps.cuny.edu for recommendations on how to pick up the necessary skill sets.

Bridge Program

If you have completed credit-bearing courses in the above areas or have used these skills at work but are no longer proficient, we offer three bridge courses: R Programming, SQL, and Data Science Math. These bridge courses are intended to refresh knowledge and skills, but are not for individuals who are learning these topics for the first time. 

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

Application Deadlines

  • Spring 2022 Priorty Deadline: November 4, 2021
  • Spring 2022 Regular Deadline: December 9, 2021
Apply Now

Student/Alumni Profiles

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

Enhancing Effective Instruction and Learning Using Assessment Data

September 16, 2021

Information Age Publishing

Dr. Jason Bryer, assistant professor and associate director of data science and information systems at CUNY SPS, is a contributor to the upcoming book "Enhancing Effective Instruction and Learning Using Assessment Data." His chapter "The Use of Predictive Modeling for Assessing College Readiness", co-written by Diana Akhmedjanova, Heidi L. Andrade, and Angela M. Lui, introduces software for automating some aspects of developmental education and the use of predictive modeling.

Moral leadership and investor attention: An empirical assessment of the potus’s tweets on firms’ market returns

September 08, 2021

Review of Quantitative Finance and Accounting

Arthur O'Connor, academic director of the CUNY SPS MS in Data Science and BS in Information Systems programs, co-authored the research paper “Moral leadership and investor attention: An empirical assessment of the potus’s tweets on firms’ market returns," which was published in the Review of Quantitative Finance & Accounting.

Understanding Linear Regression Output in R

March 23, 2021

towardsdatascience.com

Christian Thieme, a student in the CUNY SPS Data Science program, has published the article “Understanding Linear Regression Output in R” in towardsdatascience.com, a Medium publication sharing concepts, ideas, and codes.

New York Universities Ramp up Entry to Computer Science and Cybersecurity Careers

April 24, 2020

Security Magazine

Security Magazine discusses the new partnership between CUNY SPS and NYU Tandon to notify their STEM prep course graduates about our low-cost, online MS in Data Science degree program.