- What is Data Science?
- Can I enroll in the program as a part-time student?
- What is the difference between the MSDS masters program and the MS in Computer Science program at Rutgers?
- What is the prerequisite coursework for the MSDS program?
- What are the required GRE and GMAT scores?
- What is the application process? What are the deadlines?
- If I enroll in the MS in Computer Science program at Rutgers, can I transfer into the MSDS later?
- If I enroll in the MS in Computer Science at Rutgers, can I register for special MSDS course sections?
- I have attended or am currently attending a graduate school. Will my credits transfer?
- Can you give some guidance about the required recommendation letters; can they be either professional or academic?
- Is working experience necessary for entering the program?
- I notice that my TOEFL scores are below the requirement in Listening/Speaking/Reading/Writing part. Am I still eligible to apply?
- I have attached my profile (including CV, GPA, GRE/TOEFL scores). Could you please tell me whether I have any chance for admission?
- What types of careers are available for graduates?
- Does the program arrange for internships during study?
- What kind of placement services does the MSDS program provide?
- How successful has the program been at placing its students?
What is Data Science?
Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data generated at different rates and in various forms, either structured or unstructured. It uses tools from probability and statistics, data mining, machine learning, and visualization to design scalable algorithms that store, retrieve, process, analyze, visualize, encode, and summarize data at different scales. One of the goals is to facilitate computer-human-data interactions to aid well informed decision making in all data driven human endeavors.
Can I enroll in the program as a part-time student?
Yes. You may enroll in the MSDS program as a full-time or part-time student. A variety of MSDS courses are taught early mornings or 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 industry and wish to further advance their careers by upgrading their data processing systems and analysis skills.
What is the difference between the MSDS master's program and the general MS in Computer Science CS at Rutgers?
Though half of the course content required for the MSDS and MS in Computer Science programs overlap, special courses that focus on Store and Retrieval Systems, Data Interaction and Visual Analytics, Massive Data Analytics and Mining, are reserved for MSDS students. These special courses tend to be more demanding, with challenging projects and problem sets that often involve the analysis of specially curated datasets, and peer reviewed oral presentations. In addition, to the MSDS regular faculty, periodic tutorials and short classes on specialized data science topics will be taught by highly recognized industry practitioners.
Admissions and Courses
What is the prerequisite coursework for the MSDS program?
Successful applicants must demonstrate a high aptitude for quantitative reasoning. Preferably, they must have an undergraduate degree in computer science from an accredited university, and a firm grasp of mathematics and statistics at an advanced undergraduate level. It should include at least multivariate calculus, linear algebra, discrete math, and statistical methods. . More detailed information is available at our prerequisites section.
What are the required GRE and GMAT scores?
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.
What is the application process? What are the deadlines?
Students should apply through the university-wide graduate admissions office. Please see our admission section for further details on the application process, including deadlines.
If I enroll in the MS in Computer Science program at Rutgers, can I transfer into the MSDS later?
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 complete at least one competitive CapStone project.. This requirement is strict and cannot be voided.
If I enroll in the MS in Computer Science at Rutgers, can I register for special MSDS course sections?
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 Computer Science are allowed to register if they obtain permission from the MSDS program.
I have attended or am currently attending a graduate school. Will my credits transfer?
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.
Can you give some guidance about the required recommendation letters; can they be either professional or academic?
The recommendation letters can be either professional or academic. It is completely your choice.
Is working experience necessary for entering the program?
I notice that my TOEFL scores are below the requirement in Listening/Speaking/Reading/Writing part. Am I still eligible to apply?
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 retake the test.
I have attached my profile (including CV, GPA, GRE/TOELF scores). Could you please tell me whether I have any chance for admission?
Unfortunately, we cannot give any opinions about admission until we see your full application, including transcripts, test scores, and letters of recommendation.
What types of careers are available for graduates?
There is very high demand for professionals in data science in a variety of industry segments.
Does the program arrange for internships during study?
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 connections with our expanding Industry Partners Program.
What kind of placement services does the MSDS program provide?
The program will help students secure employment after graduation by utilizing its connections to recruiters and through its more formal placement services. Placement services include MSDS career days and other events, distribution of student resumé books in print and online to potential employers, and a designated recruiter's section on the MSDS website, which increases the exposure of our students to recruiters and institutions. The program also provides personalized instruction on resume writing and best practices for job interviews.
How successful has the program been at placing its students?
The program is new and has not had any graduates yet. Initial investigations have found that there is a strong demand for well-trained master's level employees with a solid methodological foundation and practical skill set in data science. We expect our students to place very well.