1. What type of background should students have?
2. What is the prerequisite coursework for the MSDS program?
3. What are the required GRE and GMAT scores?
4. What is the application process? What are the deadlines?
5. Can I start the program in Spring?
6. If I enroll in the MS in Statistics program at Rutgers, can I transfer into MSDS later?
7. If I enroll in the MS in Statistics program at Rutgers, can I register for special MSDS course sections?
8. If I enroll in the MSDS program, can I transfer to a different graduate program later?
9. I have attended or am currently attending a graduate school. Will my credits transfer?
10. Can you give some guidance about the required recommendation letters; can they be either professional or academic?
11. Is working experience necessary for entering the program?
12. I notice that my TOEFL scores are below the requirement in Listening/Speaking/Reading/Writing part. Am I still eligible to apply?
13. I have attached my profile (including CV, GPA, GRE/TOELF scores). Could you please tell me whether I have any chance for admission?
14. Do I need to send my official transcript immediately after starting my application?
15. I am currently a Rutgers undergraduate student; can my GRE/GMAT be waived?
16. What is the class size of the program this year?
17. Do most of students enrolled successfully find internships?
18. I was recommended for admission; what is my deadline to accept?
19. Will I be able to take the Ph.D. qualifying exam?
20. Does the department offer any opportunities for students enrolled in this program to engage in the professors' academic research?
1. What types of careers are available for graduates?
2. Does the program arrange for internships during study?
3. What kind of placement services does the MSDS program provide?
4. How successful has the program been at placing its students?
Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics.
Yes. You may enroll in the MSDS program as a full-time or part-time student. Most MSDS courses will be taught during the evening at the Rutgers Busch campus (in Piscataway), in order to accommodate part time students who work during the day. We encourage applications from those who are currently working in the industry and wish to further advance their careers by upgrading their statistical modeling and data analysis skills.
What is the difference between the MSDS master's program and the MS in Statistics program at Rutgers?
The MSDS program is a professional master's degree program that is designed to train students for immediate post-graduation employment in the data science industry or in data science focused departments of enterprises. The coursework and training that students receive in the MSDS program is focused on teaching statistical and related data analytical and computational tools required for data analyst positions in the data science industry. Only data science applications are used for course projects. Such in-depth training is most suitable for students with a strong interest in working as data scientists. On the other hand, the MS in Statistics program is less specialized, with coursework and training concentrating on general statistical methods. Graduates of the MS in Statistics program are prepared to continue their studies towards a Ph.D degree, or to seek employment in various industries. Though some of the course content required for the MSDS and MS in Statistics programs overlap, special course sections that emphasize data science are reserved for MSDS students. These special sections tend to be more demanding, with challenging problem sets that often involve the analysis of real data science datasets, and written projects and oral presentations, which help to hone students' skills for the data science workplace. In addition, in the MSDS program, regular faculty are supplemented by bringing in practitioners for certain courses or course sections.
Admissions and Courses
Students should have completed an undergraduate degree with a major in statistics, mathematics, or a closely related discipline, such as physics, engineering, or computer science. Students from all over the world are encouraged to apply to the MSDS program. The majority of our applicants have not worked in data science and do not already have a graduate degree, though we will consider students who have related work experience (which can be very helpful in securing an internship) or those who already have a master's or PhD in other subjects.
Successful applicants must demonstrate a high aptitude for quantitative reasoning. They must have a firm grasp of mathematics and statistics at a high undergraduate level, which includes at least multivariate calculus, linear algebra, and statistical methods. Basic skills in computer science and computer programming are also a prerequisite. More detailed information is available at our prerequisites page.
The Graduate School generally expects successful applicants to have verbal and quantitative Graduate Record Exam scores of at least 500 and 600 respectively. Our requirement for the verbal score is somewhat flexible, but successful applicants are likely to have quantitative scores considerably higher than 600. For students submitting GMAT scores, the typical requirements are a verbal score of at least 29 and a quantitative score of at least 46. Again, the requirement for the verbal score is somewhat flexible.
Students should apply through the university-wide graduate admissions office. Please see our admissions page for further details on the application process, including deadlines.
We only admit a very small number of students in the Spring -- only the very strong ones. This is due to the fact that all our courses are offered once a year and the curriculum is designed for Fall entrance. You will need to have enough background to take the Spring courses without taking the Fall courses first.
Transferring is possible, but not guaranteed. A maximum of nine credit hours of course work (which typically amounts to three semester-long courses) can be transferred to the MSDS program. Students in the MSDS program are required to take more demanding, specialized sections of some of the courses required for the MS in Statistics program. Students who have completed non-MSDS sections of these courses and who are seeking to transfer credits to the MSDS program may be required to complete additional work, such as a written project, before receiving credit for the MSDS program.
The special MSDS sections often have a very limited class size, in order to achieve the desired learning experience. The MSDS students have higher priority when it comes to registering for these sections. MSDS sections are likely to be filled in each semester. However, when space is available, students enrolled in the MS in Statistics program are allowed to register if they obtain permission from the MSDS program.
It is possible but not guaranteed. Evaluation of transfer applications to these other programs will be based on your performance in the MSDS program and other considerations. If you successfully transfer from the MSDS program to another program, credits earned in the MSDS program may be transferable as well.
There is no “transfer” from one university to Rutgers. You must first apply and be admitted. Discussion will only occur after an offer of admission has been made. Permission to transfer credit will be granted on a case-by-case basis and will not be granted automatically. Students can apply to transfer up to 9 credits for graduate courses, provided they replace appropriate courses offered by our program, and credit for such courses was not used to earn a previous undergraduate degree.
The recommendation letters can be either professional or academic. It is completely your choice.
You are still welcome to apply. Your application as a whole is more important than individual scores and weaknesses can be balanced by strengths in other parts of the application. However, if your scores are much below the requirement, we suggest that you should retake the test.
Unfortunately, we cannot give any opinions about admission until we see your full application, including transcripts, test scores, and letters of recommendation.
Please provide us with your unofficial transcript as soon as possible; any admission decisions can be made with that. However, we will need the official transcript, sent to us directly from your undergraduate institution, before you can be admitted into the program.
Yes. As part of the BS/MS continuing program, GRE/GAMT scores are waived for all Rutgers undergraduate students with 3.2 GPA or higher.
We will have approximately 40 to 50 students.
You will find this information in your offer email.
You are free to talk to any professor in the department to see if there is any opportunity. We encourage but do not promise.
There is considerable demand for people with advanced statistical training, like that obtained in the MSDS master's program, throughout the data science industry.
The program strongly encourages students to participate in summer internships with data science companies and will do its best to help students obtain internships through its dedicated Office of Professional Programs. See the Careers page.
The department of Statistics has established the Office of Professional Programs that is dedicated to the professional development and career advancement of students in the MSDS program as well as to building corporate relationships with potential employers. The Office of Professional Programs offers highly specialized services that include resume workshops, one-on-one career strategy sessions, interview preparation and exclusive internship and full-time job postings.
The program had its first graduates in 2018. Program graduates have found jobs at companies such as Microsoft, Cognizant, Bloomberg L.P., Novartis, and WePay to name a few. Job titles have included Data Scientist, Data Analyst, Analytics Manager and Machine Learning Engineer among others. We expect to place our students well.