Here you can find answers to the most common questions about the Engineering Science (Data Science) MS program.
Yes! International students are eligible to apply for the 24-month OPT STEM extension.
The Engineering Science focus in Data Science program offers both fall and spring entry. Application deadlines are posted and mark the beginning of the application review process. However, applications are accepted on a rolling basis well past the deadlines until the cohort capacity is reached.
The academic calendar is posted online on the Registrar’s website.
Yes! Students will attend two orientations: one is hosted by the School of Engineering and Applied Sciences for ALL new graduate students, and the second is a program-specific orientation that is focused on the program, degree requirements, etc.
Students can finish in 1 – 1.5 year, depending on term of entry and scheduling. Students who enroll in the fall semester have the option to finish in as early as 1 year by taking their last 6 credit hours in the summer (EAS 504 / EAS 560). Students who enroll for spring entry will graduate in 1.5 semesters. Students should talk with the graduate coordinator about their individual situations to see If 1 or 1.5 year to graduation makes more sense.
To maintain full-time status, students must take 12 credits per semester. A reduced course load may be available in certain situations, such as a student’s final semester. Students should talk with the graduate coordinator on an individual basis to see if they qualify for reduced course loads.
The program is 30 credit hours or 10 courses. It is cohort based so students will take courses in the same sequence – there is no straying from the schedule for the first two semesters. Students interested in pursuing the program on a part-time basis should consult the graduate coordinator to come up with an individualized schedule.
Students must follow the course plan as it is laid out for the first two semesters; if students return for a third semester (fall or spring) where they are part-time, students may choose to take additional courses but they will not count towards the degree requirements. After all degree requirements have been met students must apply for degree conferral and cannot continue to enroll in non-degree courses.
Yes - students can transfer up to 6 graduate-level credits. Students must have earned a “B” or higher in previous coursework, and they courses must be equivalent to coursework in the curriculum in order for the courses to be deemed transferrable. If students are interested in seeing if coursework can be transferred they should send a transcript and course syllabus to engsci@buffalo.edu.
No. The curriculum is set up with required courses and one elective (or two- if students opt for the all-course option). Students can only replace a course if they have already taken it for credit towards another degree (Example, a student takes CSE 474 Intro to Machine Learning for their undergraduate requirement and received a B or higher. In this situation the student can take an advanced machine learning course to fulfill the machine learning core requirement).
Yes! Students have the option to complete a project-based internship as part of their requirement to complete 3 credits of EAS 560 Master’s Project. Students can choose an internship or a research project with a faculty member. Students are required to seek out their own internship opportunities. The internship is a considered a culminating experience and students cannot enroll in the internship course until 2 semesters of full-time study in the program have been completed. International students must be aware of the CPT requirements and deadlines as posted on International Student Services Website.
Fall Entry:
| 1st Semester (Fall) | 2nd Semester (Spring) | 3rd Semester (Summer) | 4th Semester (optional – fall) |
|---|---|---|---|
| EAS 501 Numerical Math for Computing and Data Scientists | CSE 574 Intro to Machine Learning | EAS 560 – Master’s Project * | EAS 560 – Master’s Project * |
| EAS 502 Probability Theory | CSE 560 Data Models Query Language | EAS 504 – Data Science Survey Course * | EAS 504 – Data Science Survey Course * |
| EAS 503 Programming and Database Fundamentals for Data Scientists | EAS 509 Statistical Learning and Data Mining II | ||
| EAS 508 Statistical Learning and Data Mining I | Elective |
*Students can choose to take both EAS 560/504 in the summer to graduate in 1 year, or can take just one and extend the program to 1.5 year (which offers room to take additional courses in the 4th semester). Full time CPT is available only in the summer, as long as that semester is not the last semester in the program before graduating. Part-time CPT is available when it is the final semester of a degree program- up to 20 hours per week.
Spring Entry:
| 1st Semester (Spring) | 2nd Semester (Fall) | 3rd Semester (Winter) | 4th Semester (Spring) |
|---|---|---|---|
| EAS 501 Numerical Math for Computing and Data Scientists | CSE 574 Intro to Machine Learning | EAS 560 – Master’s Project * | EAS 560 – Master’s Project * |
| EAS 502 Probability Theory | CSE 560 Data Models Query Language | EAS 504 – Data Science Survey Course * | |
| EAS 503 Programming and Database Fundamentals for Data Scientists | EAS 509 Statistical Learning and Data Mining II | ||
| EAS 508 Statistical Learning and Data Mining I | Elective |
*Students have the option to take the project over the winter break for full-time CPT; in the final semester CPT is limited to no more than 20 hours per week.
Students can find information about tuition and fees on the student accounts website.
No we do not.
The Engineering Science MS focus in Data Science program is mostly intended for STEM students with a strong quantitative background, e.g., an undergrad degree in Computer Science, Statistics, Applied Math, Engineering, or a Natural Science. The program of study delves into the deep mathematics and computing of data science.
The Master of Professional Studies in Data Science and Applications is mostly intended for students from other backgrounds, often students with some experience in industry, who wish to learn data science and apply it to their domain of interest. This program is more focused on applications of data science.
Our graduates have gone on to become data science and analytics advisors, data analysts, data scientists, data engineers, jr. application developers, software engineers, solution engineers, operations research scientists, and algorithms engineers for companies including Dell Technologies, Amazon, FedEx, M&T Bank, ValueCentric, Roswell Park Cancer Institute, and more!
Our first cohort launched in Fall 2017. In 2020, we had our first spring cohort.
We pursue a holistic review of applications and have no hard GRE cutoff.
The Institute for Artificial Intelligence and Data Science has suspended the GRE requirement for admission to our master's and PhD programs.
Some prior knowledge of mathematics, statistics and computing (commensurate with that from an engineering/natural science/math undergraduate program) is required. Most students in the program have a solid undergraduate background in a STEM field. Entrance requirements can be found on our program homepage.
Yes, please visit the "Recent Employment of Graduate Students" dashboard and filter by Engineering Science - Data Science. This shows the employers of our first two cohorts of students. You can also scroll down to find past CPT employers too.
No, however, there are internship and research opportunities.
For curriculum-related questions, please contact the graduate coordinator at engsci@buffalo.edu. For admissions questions contact gradeng@buffalo.edu.