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Engineering Science: Focus on Data Sciences -MS

The MS in Engineering Science with a focus on Data Sciences program 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.

About Data Sciences

This applied program trains students in the emerging and high demand area of data and computing sciences. In fact, 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/science/business undergraduate program, see below for detail). The program can be completed in one calendar year of study or spread out over two years of part-time study.

The University at Buffalo has responded aggressively to these trends by first establishing a doctoral program in Computational and Data Sciences. UB has been a research pioneer in these areas and faculty have much expertise and decades of experience and assets like the world leading Centers for Computational research with unmatched facilities for big computing and data.

Entrance Requirements

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

  • Undergraduate Grade Point Average: Equivalent of a B average or better in a recognized undergraduate program; GRE: 300+ (may be waived for undergraduate degrees from accredited US/Canadian institutions or international institutions of repute)
  • Math: Calculus, Multivariate Calculus, Linear Algebra (e.g. UB course MTH 309)
  • Statistics: Basic Statistics and Probability
  • Computer Science: Programming (at least one language - C/C++/Python/Java), Data Structures (e.g. UB course CSE 113)

Students not meeting the Math/Stat/Computer Science prerequisites may request conditional admission and counselling on appropriate classes at UB or another appropriate local institution to be taken prior to starting program.

Degree Program Specifics

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.

The program may be completed in one calendar year of study or spread out over two years of part-time study.

Course Plan for Full-time Students

Fall semester – 4 core courses (Math and Stats Basics)

Spring semester – 3 core courses + 1 elective

Summer – 1 Data Science Survey course + 1 Project/Capstone

Course Requirements

Core courses  

Fall Semester: Data Science Basics

Computational Linear Algebra (3 credits)
EAS 501 (new class)

Probability Theory (3 credits)
EAS 502 (new class)

Statistical Data Mining I (3 credits)
STA 545

Programming and Database Fundamentals for Data Scientists (3 credits)
EAS 503 (new class)

Spring Semester: Data Analytics

Statistical Data Mining II (3 credits)
STA 546

Machine Learning for Data Science (3 credits) 
CSE 574

Elective 1 (3 credits)
See list below     

Databases (3 credits)
CSE 562

Summer Semester: Practicum and Survey

Data Science Survey Course**

Data Science Project***

** The Data Science Survey Course will  be offered every summer. It will include weekly modules on application-oriented and other relevant topics, including: data science for bioinformatics; data science for health informatics; data science for engineering applications; ethics and privacy; and data science for finance.

***Students will work with an affiliated faculty member on a Data Science Project. Projects will be sourced from industry where feasible.


Two out of the following courses can be selected as electives.

CSE 589 Data Intensive Computing

CSE 562 Databases

EAS XXX (new class) Exploratory Data Analysis and Visualization

CSE 535 Information Retrieval

CSE 573 Computer Vision             

CSE 601 Data Mining for Bioinformatics  

CSE 610 Deep Learning

CSE 740 Machine Learning and Big Data

CSE 674 Advanced Machine Learning


STA 517 Categorical Data Analysis

STA 546 Statistical Data Mining II  

STA 567 Bayesian Statistics


MAE XXX (new class) Simulation Analytics

MAE XXX (new class) Data in Manufacturing

MAE 609 High Performance Computing


IE 575 Stochastic Methods

IE 535 Human Computer Interaction


EE 634 Principles of Information Theory and Coding


MTH 558/559 Mathematical Finance


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