Engineering Science (Data Science) MS
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.
What Students Are Saying
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 trained professionals in these areas, estimating deficits of personnel availability in the US at as high as 150,000 a year.
Students are 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.
Program Director
Johannes Hachmann
612 Furnas Hall
engsci@buffalo.edu
This program is STEM approved, allowing international students the opportunity to apply for the 24-month STEM OPT extension.
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)
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)
Application Materials:
Students can apply online. New applicants must create an account. If you have applied in the past, you will be asked to log in to your existing account to start a new application.
The application includes sections that will ask you to provide us with personal/biographical information, contact information, citizenship details, your past or current college/university, and degree details. You will be able to enter the names and contact information of your recommenders and upload supporting materials to your application. You may upload supporting documents while completing the application form or after you have submitted your application in your Application Status Portal.
You do not need official transcripts or test scores to apply. Unofficial copies are sufficient for application review, but students are encouraged to provide official copies if they have them.
A non-refundable fee of $100 is required to apply for Spring 2025 or later terms. The application fee for Fall 2024 is $85. After you submit your application form, you can pay the application fee online in your Application Status Portal with a credit card or e-check.
Copies of transcript(s) for all post-secondary schoolwork must be uploaded with your application for initial review. Upon an offer of admission, accepted applicants will be required to submit official transcripts and proof of degree(s).
Two letters of recommendation are required to apply to this program. Letters are automatically requested when you enter recommenders' names and email addresses in your application. While we will accept letters from professional sources, we strongly prefer letters from professors acquainted with your academic interests, achievements, and abilities.
GRE scores are optional but recommended for this program. If you would like to take the GRE, you can arrange to take it through the Educational Testing Service (ETS). Official scores should come directly from ETS.
Note: the University at Buffalo Institutional Code is 2925.
Upload a resume or curriculum vitae (CV) to your application. Your resume or CV should include details about your education, employment, and internship history. Pertinent research experience should be included as well.
International applicants are required to provide proof of English proficiency. The chart below outlines acceptable test types and the university's minimum score required for admission. All applicants whose native language is not English will be required to provide proof of English proficiency.
Copies may be submitted for initial review. Official scores must be sent directly to UB from the testing agency.
| Test Type | University Minimum Score | To Order... |
|---|---|---|
| TOEFL (IBT) (including MyBest scores) TOEFL Home Edition | 79 | Use Institution Code 2925 |
| TOEFL Essentials | 8.5 | Use Institution Code 2925 |
| TOEFL ITP Plus | 550 | Use Institution Code 2925 |
| IELTS and IELTS Indicator | 6.5 | Select "University at Buffalo, State University of New York (SUNY)" |
| 55 | Select "University at Buffalo, State University of New York (SUNY)" | |
| Cambridge English Proficiency (CPE) | 185 | Select "University at Buffalo, State University of New York (SUNY)" |
| Cambridge English Advanced (CAE) | 185 | Select "University at Buffalo, State University of New York (SUNY)" |
| Duolingo English Test (DET) | 120 | ADA Department of Testing will report your official scores to central application services |
Exam results must be dated within two years from your proposed date of admission and remain valid upon entering the term for which you applied.
It is strongly recommended to make test arrangements early in the year so sufficient time can be allowed for the results to be reported before application deadlines.
Exemptions to English Language Proficiency requirements can be found on The Graduate School website.
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
Please do not mail application materials. All items should be submitted electronically with your online application. Please log in to your Application Status Portal frequently to ensure that all of their supporting documents have been received.
- 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
Core Courses (all are 3 credit hours each)
First Semester: Data Science Basics
This course provides basic background on probability theory at a beginning graduate level. Topics include introductory probability concepts, discrete and continuous random variables and probability distributions, joint probability distributions, random sampling and data description, point estimation of parameters, random variables, derived probability distributions, discrete and continuous transforms and random incidence. As time permits, the course introduces elementary stochastic processes including Bernoulli and Poisson processes.
The aim of this course is:
- To develop the ability to formulate and solve problems using mathematical methods and tools
- To apply knowledge gained in lower-level mathematics courses
- To introduce concepts and methods of linear algebra
- To introduce a broad range of numerical methods
- To develop an ability to identify, understand, and solve algebraic equations
- To develop an ability to identify, understand, and solve differential equations
- To develop experience with numerical and symbolic mathematical software and their use in problem-solving
An introduction to the mathematical theory and computational methodology at the heart of statistical learning. Using a Bayesian paradigm, this first semester considers supervised learning, including topics of classification - support vector machines, k-nearest neighbors, Naive Bayes, logistic regression, tree methods and forests, bagging and ensemble methods – as well as Gaussian processes and neural networks, and methods for validation and testing. The R programming language will be used. Students will develop a facility for statistical learning of data; students will become proficient in writing computer code to analyze datasets and draw conclusions from analysis.
This course has both a traditional lecture component, as well as an online computational lab component; labs will be run approximately every third week.
Second Semester: Data Analytics
An introduction to the mathematical theory and computational methodology at the heart of statistical learning. Using a Bayesian paradigm, this second semester considers unsupervised learning, including dimension reduction, clustering, Gaussian mixtures methods, graph models, and model averaging. The course will examine parametric and non-parametric regression, including Gaussian Process regression. The R programming language will be used. Students will develop a facility for statistical learning of data; students will become proficient in writing computer code to analyze datasets and draw conclusions from analysis.
This course has both a traditional lecture component, as well as an online computational lab component; labs will be run approximately every third week.
Involves teaching computer programs to improve their performance through guided training and unguided experience. Takes both symbolic and numerical approaches. Topics include concept learning, decision trees, neural nets, latent variable models, probabilistic inference, time series models, Bayesian learning, sampling methods, computational learning theory, support vector machines, and reinforcement learning.
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.
See a list of elective courses below.
Third Semester: Project and Survey
This course will provide students with an overview of data-driven analytics in different industry sectors. The class will have a series of visiting lecturers with the faculty member teaching the class providing an overview, continuity and grading of homework and term papers.
*The Data Science Survey Course 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.
This course will provide students with a final integrative project experience. The class will require students to obtain an integrative project experience either in industry or at the university. In either case, the students will use the skills acquired during the other classes in executing project goals. Students will provide short reports to supervising faculty to ensure that learning objectives are being met.
**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 531 Algorithms Analysis + Design
CSE 535 Information Retrieval
CSE 546 Reinforcement Learning
CSE 562 Database Systems
CSE 573 Computer Vision
CSE 586 Large-Scale Distributed Systems
CSE 587 Data Intensive Computing
CSE 601 Data Mining for Bioinformatics
CSE 633 Parallel Algorithms
CSE 635 NLP and Text Mining
CSE 676 Deep Learning*
*Students must have successfully completed CSE 574 before taking CSE 676. Cannot be taken in the same semester as CSE 574.
CSE 674 Advanced Machine Learning
STA 517 Categorical Data Analysis
STA 567 Bayesian Statistics
CDA 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
Questions?
For degree-specific related questions, please contact the Graduate Coordinator at engsci@buffalo.edu.
For admissions-related questions, plesae contact gradeng@buffalo.edu.