|Subjects||Statistics||Algorithmic Foundations||Tools/Syst for Massive Data||Knowledge Discovery||Visualization|
|Required Courses: 5 Total||Stat581: Probability & Statistics for Data Science||CS512: Data Structures & Algorithms||CS543: Massive Data Storage & Retrieval Tools
(taught by MSDS faculty)
|CS550: Massive Data Mining and Learning
(taught by MSDS faculty)
|CS526: Data Interaction and Visual Analytics
(taught by MSDS faculty)
|Required Capstone I CS554||
First semester of the 2nd year: Hands-on Experience; Project and Industry Exposure (taught by MSDS faculty)
|Elective Courses: (at most 2 per area)||Stat 697: Data Wrangling and Husbandry||CS 521 : LP/Optimization||CS 539: Databases||CS 520: AI||CS 534: Computer Vision|
|Stat 596: Intermediate Statistical Methods -- Regression and Time Series||CS 513/514:Advanced Algorithms||CS 546/547:Security/Privacy||CS 535/536: Machine Learning||CS 523: Computer Graphics|
|Elective Capstone II CS555 or Independent Studies||
Second semester of the 2nd year: Hands-on Experience Project
Pre-requisite:Excellent performance in Capstone I(taught by MSDS faculty)
Up to three credits of Independent studies(604,605,606)
This Professional Master program in Data Science, rather than just adapting to the advent of Big Data, is an analytical degree program designed from the ground up to focus on the latest systems, tools, and algorithms to store, retrieve, process, analyze, visualize, and synthesize large data. It consists of six foundational classes and six elective courses. Every student is required to complete before graduation a competitive one semester Capstone Project. A central goal of the program is to build systems that integrate in a coherent manner the full data cycle: from data gathering to data visualization and data synthesis aided by computer-human interaction.
The six foundational classes expose students to the identification of questions whose answers can be aided by data retrieval, data cleaning and data modeling tools, plus specialized algorithmic and statistical processing, machine learning, pattern recognition and interactive visualization tools. A faculty supervised CapStone class is dedicated to building prototype systems where students exercise the skill set acquired in the other foundational classes.
The six remaining elective courses offer students the opportunity of further specializations in Statistics, Algorithms, Optimization, Machine Learning, Data Privacy/Security, Computer Graphics and Vision. An elective second Capstone project can be used, at the student's discretion, to compete in a Master wide context in Data Science.
This Masters in Data Science Program within the Computer Science Department provides its graduates with:
I. A deep understanding of the algorithmic tools and computational infrastructure necessary to build systems that manage, explore, and make sense of uncertain and complex data.
II. Computational skills to apply inference principles and prediction techniques to extract actionable value from data repositories.
III. Strong technical skills for data analytics, visualization, and summarization.
IV. The necessary communication and leadership skills for successful careers as data science professionals
It is an exciting program that provides its graduates a reliable path to a rewarding career as professional data scientists.
Market: As modern information technologies relentlessly generate voluminous and complex data, algorithmic analytical tools with solid foundation in computer science, statistical theory and computer human interaction and communication have become indispensable to industry and society in general. This trend has created a strong surge in the demand of professional data scientists.
Essential data scientists skills include computational thinking, interactive data collection, pattern exploration, statistical analysis, summarization, visualization and sense making. One of the central goals is to create and integrate computerized tools that enhance decision making in social, scientific and economical endeavors aiming to improve citizens quality of life.
Receive solid training in algorithms, probability, statistical learning, computing systems, data mining, machine learning and visualization.
Acquire a deep understanding of the nature of uncertainty, modeling, performance checking and decision making.
Implement interactive tools for data analysis and sense making.
Develop strong communication and leadership skills.
Enroll in six Foundational Classes and six Elective Classes. They include the completion of one required Capstone project and a second optional Capstone Project to participate in an open competition in Data Science. Participate in Summer Internships.
Possible summer internships
Students with a B.S in computer science, engineering, mathematics, or related fields.
Departments of Computer Science, Rutgers University.
Experienced faculty members and specialized teaching faculty.
Exclusive and specially designed courses, projects, guest lectures, field trips etc
Excellent connections to industry
Enhanced learning facilities, dedicated associate director of administration
For the incoming sprng 2017, we are offering admission to prospective students that satisfy the following admission criteria:
a. Courses in an accredited university with an equivalent grade of B or better in Multivariate Calculus, Discrete Math, Linear Algebra, Statistics, Probability
b. Language Proficiency in : (C/C++, Java) or (Python - Perl) and a Unix/Linux Shell
c. An undergraduate degree in Computer Science from an accredited university with a GPA greater than or equal to 3.25 (out of 4).
d. Students with undergraduate degrees in Statistics, Mathematics, Physics, Engineering and other Sciences with a GPA above 3.5 (out of 4) , may be considered for temporal admission and will be placed in undergraduate bridge classes on Data Structures and Algorithms, Data Bases, Operating Systems and Computer Architecture. After successful completion of these remedial classes (with a grade of B or better) they will be granted full admission into the MSDS Program.
Materials required for the MS Data Science application include:
Three letters of recommendation
Test scores from general GRE exam or GMAT exam
TOEFL or IELTS test scores
(if non-native English speaker - details of who must submit a TOEFL/IELTS score may be found at this link)
Official Test scores (TOEFL/GRE) must be sent directly from ETS to Rutgers University (use code 2790 for Rutgers university, no code for our department is needed). GMAT scores must be sent to the Graduate School of New Brunswick.
Important! Admitted applicants who do not meet the prerequisite requirement must take the appropriate courses at their home institution or online before arriving at Rutgers in the fall. A placement test will be given to test the readiness of the students. Failure to pass this test may significantly delay the student's progress in the program.
Official transcripts and other material which is not submitted online should be mailed to:
Office of Graduate and Professional Admissions
Rutgers, The State University of New Jersey
56 College Ave
New Brunswick, NJ 08901-8541, U.S.A.
The Graduate School generally expects successful applicants to have verbal and quantitative Graduate Record Exam scores with a mean of 150 and 160, respectively. Successful applicants to the MSDS program are likely to have quantitative scores considerably higher than 600/148. For students submitting GMAT scores, the typical requirements are a verbal score of at least 29 and a quantitative score of at least 41. However, there is often some flexibility for the verbal score for both GRE and GMAT.
TOEFL score of successful applicants had a mean of 92, IBT S >21 IBT L >21
The acceptable IELTS score bandwidth is 7.
For the Spring semester: International, September 15th. MS applications only ; United States citizens as well as green card holders: October 15th.
For the Fall semester: February 1st for International MS applicants; United States citizens and green card holders MS applicants: March 1st.
Start your application at the Graduate Admission website:
The MSDS(CS) program requires students to participate in extensive practical training as an essential component of the degree program. The program requires all full-time students to participate in ongoing practical training immediately following their initial enrollment. The requirement does not require registration for additional credit but does require registration by ALL students, regardless of citizenship or visa status, for the zero credit practical training course for each semester or summer session in which a student does an internship or coop. Practical training can be fulfilled by any one of the following methods with off-campus internships or coops being strongly recommended as the preferred mode of fulfilling the practical training requirement:
1. Strongly Recommended: While not required, an off-campus Internship or Co-op position with an employer in the data science industry or data science related department of a company is STRONGLY RECOMMENDED as the most preferred component of Practical Training. The position should be related to the Data Science curriculum and is at least four weeks in duration. The student is required to submit a written report after the internship ends. If approved by the program director, such an internship can be part of an elective independent study course and earn credits towards degree.
2. Regular full-time position with an off-campus employer in the data science industry or data science related department of a company. The position should be related to data science and be at least four weeks in duration. If approved by the program director, such employment can be part of an elective course under independent study course and earn credits towards degree.
3. On-campus research, or research project participation, where the research or project is related to data science under the sponsorship of one or more data science institutions, industry practitioner or a Rutgers faculty member (regular or adjunct). A detailed written report on the research or project participation should be submitted and approved by a faculty member. If approved by the program director, such research or project participation can be part of an elective course under independent study course and earn credits towards degree.
4. Participation in the MSDS(CS) practitioner's seminar series or MSDS(CS) career workshop series. Students must attend at least four of the seminars from either series. A written report should be submitted based on one of the technical seminars. The report should summarize the problems, ideas, approaches and results learned from the seminar, and provide additional information from further reading and research of the topic.
International students should be aware of the United States Citizenship and Immigration Services rules governing off-campus employment in the United States for students with F-1 visas. Please consult staff members at the Center for Global Services for advice well in advance of internship search and employment. Also, international students should attend an Off--Campus Employment Workshop early in the semester prior to their beginning CPT employment. These workshops are offered twice a month by the Center for Global Services - check here for the schedule. In addition, depending on the availability of the MSDS(CS) Global Services Center advisor, a customized CPT workshop for MSDS(CS) students will be scheduled in each semester.
Adopted January 2016
The Graduate Program in Data Science trains Master of Science students on the latest systems, tools, and algorithms to store, retrieve, process, analyze, visualize, and synthesize large data. The graduate program also meets the expectations and standards as outlined by the professional organization providing specialized accreditation for the discipline.
Learning Goal 1 for Students: Attain mastery of the essential aspects of practice and research in the field of data science
Assessment of student achievement of Goal 1:
• Grades in graduate courses
• Review by faculty of student progress with close advising and mentoring
• A Capstone Project implementing a computer system that integrates in a coherent manner the full data cycle: from data gathering to data visualization and data synthesis aided by computer - human interactions.
- Assessment of student's performance by committee of graduate faculty members
• Continuation of graduate studies or placement in a professional position that requires an understanding of the norms and practices of data science, as well as competency in the technical aspects of performing the work associated in the field.
Roles of the program in helping students to achieve Goal 1:
• Close advising to assure that students are being prepared in a coherent and academically rigorous way
• Effective monitoring of student progress
• Evaluations of teaching effectiveness of instructors in graduate courses
- If effectiveness is below expectations, work with instructors to improve effectiveness
• Periodic review of curricular offerings, degree requirements and assessment tools
- By program faculty
- In consultation with the office of the dean of the graduate school and/or the unit dean
Learning Goal 2 for Students: Prepare to be professionals in data science
Assessment of graduate student achievement of Goal 2:
• Participation in internships and other work tailored to career goals, assessed accordingly
• Collection of data on professional placement data or continuation of graduate studies
• Review by external advisory committees, both inside of and external to academy.
Role of the program in helping students achieve Goal 2:
• Host professional development and career exploration activities
• Develop internship opportunities
• Host external advisory committees
The Advisory Board of the Data Science Program will regularly review the structure and content of the program and the feedback received from assessments and surveys. These reviews will be used to provide the best possible education to students that meet current needs for professionals in data science.
MSDS Students has to complete 36 credits (12 courses) divided as follows:
Five required courses.
One required capstone project.
Six elective courses, at most two per area. This can include:
Elective courses in MSDS
Elective capstone project
Besides the courses, a student can choose a Thesis option(Counts as two courses)
To complete the MS degree, students must present 36 credits satisfying the requirements listed above, and yielding at least a B average.
Thesis option The student must write a masters thesis, and must register for exactly six credits of 198:701-702. The Master's thesis must be a written account of a critical and scholarly investigation in an area of computer science. It may represent: (a) a piece of independent research (extensions and improvements of work in a given part of the field are acceptable at a level of novelty which is less than that required for a doctoral thesis); (b) a work of synthesis that gives new significance and insight to previously-known results; or (c) an important constructive contribution to the development of a computer application. The thesis may not be a digest of known results from the literature, a summary of a published report, company classified or government classified material, or dependent for its background on other non-available reports. The thesis topic should be chosen by mutual agreement between the student and a member of the faculty. The thesis must be approved by the student's thesis committee. The committee consists of the thesis supervisor and two other faculty members who are appointed by the Graduate Committee in consultation with the thesis supervisor. The advisor and members of the thesis committee must be members of the Graduate Faculty in Computer Science.
The thesis must be written in English (except that some portion may require a computer language, with English documentation), it must be the student's own work and it must demonstrate the student's facility for expository writing.
There are several forms that must be filled out and submitted by the appropriate deadlines in order for the student who has fulfilled the above requirements to receive a Master's degree. (The approximate deadlines are listed in section 9.) It is the student's responsibility that all this paperwork be done on time. Information and forms can be obtained from the graduate secretary.
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.
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.
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?
No, Work Experience is not necessary for entring the MSDS 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.