Data Science, MS

Program Overview

Degree Awarded

Master of Science in Data Science

Chairs

Joseph Mitchell, Samir Das

Director

Zhenhua Liu

Coordinator

Christine Rota

Office

Math Tower Rm P-138 & 1-106

Program Website

The SBU Graduate Program in Data Science (DAS) features both MS and PhD degree programs in Data Science. It is jointly offered by the Department of Applied Mathematics and Statistics (AMS), and the Department of Computer Science (CS), both part of the College of Engineering and Applied Sciences (CEAS). Students will receive vigorous training in Data Science encompassing topics such as statistical analysis, big data analysis/management and fundamentals of computing.

Application Requirements

Applying to the Program

We welcome students with solid foundations in data science, computer science, statistics, mathematics, or related science or engineering disciplines to apply for our program. General application information can be obtained at the Graduate School website or go directly to the application website. Please view this website for domestic and international application deadlines.

Application Requirements

• A bachelor’s degree in computer science, data science, statistics, mathematics, a natural science, engineering, an applied science, or social science with a strong mathematics and Students are expected to be fluent in Calculus and at least one programming language.
• A minimum overall cumulative GPA of at least 3.0 (or equivalent), and at least a 3.0 average in all courses in pertinent or related fields. Attention will be given to courses related to data science, computer science, statistics, and mathematics.
• For Fall 2023 applications, the Graduate Record Examination (GRE) General Test is NOT required. However, you are most welcome to submit your GRE score should you have it.
• An applicant who is not a native or primary speaker of English must present a minimum score for either the TOEFL or IELTS tests as follows:
  • TOEFL iBT: Overall score of 90 for doctoral applicants and 80 for master’s applicants.
  • IELTS: Overall score of 6.5, with no subsection recommended to be below 6.
• Three letters of reference and all transcripts of undergraduate and/or graduate study completed.
• Applicants with domestic credentials must submit an official transcript from each undergraduate college or university attended, regardless of whether a degree was conferred. Applicants must also submit an official transcript from each college or university relating to graduate-level work, regardless of whether a degree was conferred.
• Applicants with international credentials must submit an official English translation of all coursework showing a complete course-by-course record, GPA, degree, and institution, in addition to the original documents.

Degree Requirements

The MS program features 36 credits (12 courses) including 10 core courses and 2 electives.

Core Courses

• AMS 507 - Introduction to Probability (3 credits)
• AMS 572 - Data Analysis I (3 credits)
• AMS 580 - Statistical Learning (3 credits)
• AMS 597 - Statistical Computing (3 credits)
• CSE 581 - Computer Science Fundamentals: Theory (3 credits)
• CSE 582 - Computer Science Fundamentals: Data Structures and Algorithms (3 credits)
• CSE 583 - Comp Sci Fundamentals: Prog.Abstract. (3 credits)
• ISE 503 - Data Management (3 credits)
• AMS 560 - Big Data Systems, Algorithms and Networks (3 credits)
• AMS 598 - Big Data Analysis (3 credits)

Electives

The two electives can be selected from any letter-graded courses offered by the AMS and CS departments. As a capstone experience and part of the electives, we require the students to take either (i) 3-credits of AMS 585 - Internship in Data Science, or (ii) 6-credits of AMS 585 _ _ as a master thesis, with a faculty mentor.

Course Plan

Students are expected to complete their MS program in 3 to 4 semesters. The following is a 3-semester course plan for MS in Data Science.

Term 1 (Fall semester)

• AMS 507 - Introduction to Probability (3 credits)
• AMS 572 - Data Analysis I (3 credits)
• CSE 581 - Computer Science Fundamentals: Theory (3 credits)
• CSE 582 - Computer Science Fundamentals: Data Structures and Algorithms (3 credits)

Term 2 (Spring semester)

• AMS 580 - Statistical Learning (3 credits)
• OR AMS 530 - Principles in Parallel Computing (3 credits)
• AMS 597 - Statistical Computing (3 credits)
• CSE 583 - Comp Sci Fundamentals: Prog.Abstract. (3 credits)
• ISE 503 - Data Management (3 credits)

Term 3 (Fall semester)

• AMS 560 - Big Data Systems, Algorithms and Networks (3 credits)
• AMS 598 - Big Data Analysis (3 credits)
• Elective 1
• Elective 2

Financial Support

All students admitted to the PhD program will automatically be considered for financial support and tuition waiver if the application is submitted by the due date. MS students are not typically considered for financial support or tuition waiver at the time of admission. However, in a small number of cases qualified MS students are able to receive partial support via departmental or campus opportunities.