Here you can find answers to the most common questions about the ICDS Master of Professional Studies (MPS) in Data Sciences and Applications.
Yes! International students are eligible to apply for the 24-month OPT STEM extension.
Our first cohort launched in Fall 2019!
The students enrolling in this program come from a broad range of backgrounds. Notably, student backgrounds do not need to be from a STEM major such as Engineering or Mathematics. It is recommended that students have some exposure to calculus, probability and/or statistics and computer programming, but they do not need to have formal training in all of these. Industrial experience is also valuable, but not required.
The Data Science and Applications program only offers fall intake.
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 how students choose their final semester scheduling. All full-time students will take 12 credits in the Fall semester and 12 in the Spring semester. Students have the choice to finish in 1 year by completing both their Project Guidance (internship or research with a faculty member) and Major Applications course requirements in the summer.
Alternatively, students can finish in 1.5 years by taking their project course (internship) and major applications course in the fall. Another 1.5 year option is to complete a project course as a full-time summer internship, and complete the Major Applications course in the Fall. The latter choice is ideal for students on an F1 visa, which carries a stipulation on the number of hours of intership that can be completed in a final semester of the program.
No, students cannot have full-time CPT if it is their last semester of study, and students must complete 2 semesters of full-time study before becoming eligible for CPT.
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 (or fourth semester, depending on summer registration) 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 firstname.lastname@example.org.
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 CDA 571 Project Guidance. 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.
Yes, you can for both 1 year and 1.5 year program options. The Major Application course is offered online to students that are away for their internships (Project Guidance). Therefore, the courses can be taken simultaneously or in sequence.
|1st Semester (Fall)|| |
2nd Semester (Spring)
|3rd Semester (Summer)||4th Semester (optional – fall)|
|EAS 503 Introduction to Data Driven Analysis||CSE 574 Intro to Machine Learning||CDA 561 Major Applications*||CDA 561 Major Applications*|
|CDA 541 Statistical Data Mining I||CDA 511 Intro to Numerical Analysis||CDA 571 Project Guidance*||CDA 571 Project Guidance*|
|CDA 502 Database Management Systems||CDA 546 Statistical Data Mining II|
|CDA 531 Probability Theory||CDA 551 Cybersecurity, Privacy and Ethics|
*Students can choose to take both CDA 561/571 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, if student is interested in additional courses outside of the degree requirements.
Students can find information about tuition and fees on the student accounts website.
Both programs are driven by some of the more cutting edge topics and research in data science. They are exactly the same length and both have requirements for a capstone project (which includes the opportunity for students to complete a project-based internship in industry!). The fundamental differences are the backgrounds of students entering each program and the specific course requirements.
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. Nearly all courses students taken in this program come from the School of Engineering and Applied Sciences.
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. The course requirements are more interdisciplinary in nature, as they are from the School of Engineering and Applied Sciences, School of Management, School of Public Health and Health Professions, School of Pharmacy, and the College of Arts and Sciences.
We pursue a holistic review of applications and have no hard GRE cutoff. This review includes transcripts, letters of recommendation and industry experience.
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. Entrace requirements can be found on our program homepage.
Not as of yet, our first cohort will graduate in September 2020
For curriculum-related questions, please contact the graduate coordinator at email@example.com. For admissions questions contact firstname.lastname@example.org.