Rochester Institute of Technology Online Master of Science in Data Science, New York

Why Rochester Institute of Technology?

The online Master of Science in Data Science offered by Rochester Institute of Technology provides preparation for data science careers combining a quantitatively rigorous core curriculum with the opportunity to learn from experts across a range of technical electives. Rochester Institute of Technology,  a private research university near Rochester, New York, expects applicants to hold an undergraduate degree with a minimum GPA of 3.0.  Prior study or professional experience in computer programming is required, or students without this background but an otherwise strong application can complete bridge courses offered by the Institute.  GRE and TOEFL scores are required only from international applicants. Domestic applicants do not need to submit test scores.

Program Snapshot

University Name Rochester Institute of Technology
Location Rochester
State New York Grade A-
Name of Degree Master of Science in Data Science
School or Department Administering Program Software Engineering
Credits 30
Cost per credit $1,191
Tuition for Entire Program $35,730
Test Requirements
  • TOEFL if applicable
  • GRE required for students with degrees from international universities
Minimum GPA 3.00
Prerequisite Courses and Skills Prior study or professional experience in computer programming or complete bridge courses
Campus Visit Required? No
Typical Time to Complete Two years
When Can Students Start the Program? Fall, Spring
Program Concentrations None
Synchronous Classes No
Required Courses
  • Introduction to Data Science: Management
  • Foundations of Data Science
  • Software Engineering for Data Science
  • Applied Statistics
  • Applied Linear Models – Regression
  • Graduate Capstone
Does the Program Include a Capstone? Yes
Does the Program Include a Practicum or Internship? No
Other Features that Make the Program Unique
  • Can do program combined with EdX micromasters
  • Same faculty that teach in-person teach online
  • Enhanced personalized academic advising
  • Online concierge service for student questions outside of coursework
Program Objectives 
  • Machine learning skills and ability to build and fine-tune predictive models
  • Skills in programming in Python and R to synthesize and manage big data
  • Techniques such as data mining, text mining, and regression analysis
  • Creation, assessment and communication of data visualizations 
Program Description This 30 credit program prepares students for careers in data science across a range of organizations. Students learn concepts and skills of machine learning and predictive analytics, as well as programming in Python and R, and techniques in data mining, text mining, and visualization. A capstone is required.

What will I Learn?

The Rochester Institute program is somewhat flexible since students complete electives in addition to required courses. The program aims to develop data scientists who possess skills in machine learning, programming, data mining, regression analysis, text mining, predictive analytics, and visualization. The aim throughout is to prepare students to synthesize large data sets and build predictive models in order to discover insights from the data and drive decision-making forward in your organization.

All students must complete courses in Introduction to Data Science: Management and Foundations of Data Scientists. Foundational mathematical and statistical concepts are taught in Applied Statistics, Applied Linear Models, and Regression. Advanced techniques are the focus of courses on Software Engineering for Data Science. Students then have an opportunity to apply the skills and knowledge they have learned through a final capstone project exploring a data-intensive problem. Students explore domain knowledge and application of data science through elective coursework drawing on the large and experienced faculty of RIT, which is known particularly for its software engineering, film and imaging, hospitality, and video game design expertise, among other fields.

Students in the Rochester program can also pursue an alternate pathway to the degree, which includes completing the MIT EdX Micromasters in Statistics and Data Science. Students take 21 credits from RIT and nine credits from the Micromasters.

How much will the program cost?

Students finish their degree within two years and pay $1,191 per credit for a total of 30 credits, or just over $35,000.

How does the program fit into my life?

All courses are asynchronous, meaning that instructors create learning materials such as lectures, activities, quizzes, assignments, and group discussions in weekly modules, and students are responsible for completing the module on their own schedule within the given week.  The program can be completed 100% online, and no campus visits are required.  The program is usually completed in 2 years. Students can enter the program in Fall or Spring. Rochester Institute offers online students enhanced personalized academic advising, helping your program to run more smoothly and to be a more rewarding educational experience.  An online concierge service to help answer non-academic questions related to your experience is also offered.


Students interested in learning from one of the foremost schools in the country in the fields of computer science and engineering may jump at the chance to attend the Rochester Institute’s master’s program in data science. A rigorous curriculum requiring substantial coursework in math and statistics, along with access to a wide range of electives, sets the RIT program apart. Some students may find the opportunity to apply EdX credits to their degree very appealing, and the program may also be attractive if you are interested in one of the fields in which RIT excels academically. The program does not offer named concentrations, giving students more freedom in course choice. Students who wish to be able to demonstrate domain expertise to employers through a named concentration on their degree may wish to look elsewhere, as will students with less quantitative educational background.