Engineering Science (Data Science) MS

Students analyzing data on various devices.

The Engineering Science MS with a focus in Data Science provides students with a core foundation in big data and analysis by obtaining knowledge, expertise, and training in data collection and management, data analytics, scalable data-driven discovery, and fundamental concepts.

The program is designed for students with engineering, natural science, or mathematical science backgrounds.

About the Program

This applied program trains students in the emerging and high demand area of data and computing sciences. Many surveys of employment have highlighted the great need for suitably trained professionals in these areas, estimating deficits of personnel availability in only the US at as high as 150,000 a year.

Students will be trained in sound basic theory with an emphasis on practical aspects of data, computing and analysis. Graduates will be able to serve the analytics needs of employers and will be exposed to several areas of application. The degree can be specialized using electives and a project. Classes will be modestly sized and emphasize best classroom practices while employing online resources to reinforce the classroom experience.

Students in this program will need some prior knowledge of mathematics, statistics and computing (commensurate with that from an engineering/natural science/math undergraduate program, see the entrance requirements below for details). The program can be completed in one calendar year of study.

STEM Approved

This program is STEM approved, allowing international students the opportunity to apply for the 24-month STEM OPT extension.

Program Director

Johannes Hachmann
612 Furnas Hall

Admissions Requirements

Some prior knowledge of mathematics, statistics and computing (commensurate with that from an engineering/natural science/math undergraduate program) is required.

Undergraduate Grade Point Average:

Equivalent of a B average or better in a recognized undergraduate program; GRE: 300+ (waived for recent UB undergraduate students)


Calculus, Multivariate Calculus, Linear Algebra (e.g., UB course MTH 309)


Basic Statistics and Probability

Computer Science:

Programming (at least one language - C/C++/Python/Java), Data Structures (e.g., UB course CSE 113)

Application Deadlines

We accept applications on a rolling basis throughout the year, but encourage all prospective students to submit their applications by the deadlines noted below.

Spring enrollment: 
Apply by October 1 

Fall enrollment: 
Apply by February 15

Application Materials

Please do not mail application materials. All items should be submitted electronically with your online application. Please login to the Application Management System frequently to ensure that all of their supporting documents have been received.

Degree Program Specifics

  • This program is currently taught in a cohort-based model and offers both Fall AND Spring admission.
  • Students will take a combination of core courses (18 credits), electives (6 credits), a data science survey + capstone course (3 credits) and the data science project (3 credits) for a total of 30 credits.
  • Students have the opportunity to complete an internship in industry for their data science project requirements. Alternatively, students can opt to complete a research project with a faculty member.
  • The program may be completed in one calendar year of study.

Course plan for full-time students:

  • First semester – 4 core courses (Math and Stats Basics)
  • Second semester – 3 core courses + 1 elective
  • Third semester – 1 Data Science Survey course + 1 Project/Capstone

Course Requirements

Core Courses (all are 3 credit hours each)

First Semester: Data Science Basics

Second Semester: Data Analytics

The course focuses on the issues of data models and query languages that are relevant for building present-day database applications. The following topics are addressed: Entity-Relationship data model, relational data model, relational query languages, object data models, constraints and triggers, XML and Web databases, the basics of indexing and query optimization.

Third Semester: Project and Survey

Have questions or want to learn more?

For degree-specific related questions, please contact the graduate coordinator at

For admissions-related questions, plesae contact