Nov 26, 2024

Graduate Catalog 2020-2022

Graduate Catalog 2020-2022 [ARCHIVED CATALOG]

Data Science, M.S.

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Program Description

The 36-credit interdisciplinary Data Science Master’s Degree provides a strong background in data science methodologies. The program has strong emphasis on the areas of programming, data management, data mining, machine learning and statistics. The degree culminates in a course in project management, a real world internship experience and a two-semester capstone Data Science Project.

The data science technologies have completely changed the way businesses do strategic planning to manage their operations more efficiently and to serve their customers more effectively. These developments have created tremendous opportunities for innovation by tapping the enormous potential for the benefit of society. Graduates will be prepared to participate in these innovative opportunities.

The program endeavors to maximize interaction between faculty and students.

Program Requirements

Data Science concerns itself with volume, variety and velocity of data being generated. It is about understanding data, processing, and discovering value in data, visualizing it and communicating results. This is applicable to many areas such as marketing, business intelligence or health care management, and a professional skills component

There are 36 credits total in the degree, which can be completed in four semesters by a student taking nine credits per semester. Classes are scheduled in the evening making it easier for working professionals. A student can also complete the degree at a slower pace if so desired.

All students will take a set of core courses in three areas: Statistics, Computer Science and Data Science. Students will also complete plus courses (as defined by the SUNY Professional Science Master’s initiative), a project report and an internship. There are six credits of statistics, which will include modeling and use of software packages. The Computer Science component will be nine credits in algorithms and programming, database, and data warehousing. The six credits of Data Science will cover the standard and up-to-date techniques of Machine Learning and Data Mining with examples using real world data. The plus courses will teach students necessary skills for the workplace, such as Project Management and Communications and Presentation. For the applied portion of the degree, students will work in teams using all of their previously gained knowledge to solve a real world problem demonstrating the data science cycle. Additionally, students will be required to complete an internship.

Program Learning Outcomes

The program’s primary student learning outcomes are:

Students will demonstrate knowledge of efficient algorithmic processes and “good” programming skills in several languages used in the implementation of efficient solutions to big data problems.
Students will demonstrate the ability to clean data, integrate data from distinct sources and store the data in the appropriate databases for analyses.
Students will be able to do basic and advanced statistical analysis on big data sets. This knowledge will help the students to develop solutions to complex big data problems.
Students will demonstrate strong written and oral communication skills.
Students will demonstrate the ability to understand the big picture of a complex data science problem and analyze the data to answer undirected and directed questions.
Students will demonstrate the ability to function as a team member and to lead effectively in challenging management positions in local and global environments.
Students will demonstrate their ability to apply the skills and the knowledge gained in the classroom to real world data science problems.

Degree Requirements

The largest component of the degree is the Core which includes the three disciplines of Statistics, Computer Science and Data Analytics. The Plus Courses and the Culminating Experience are the business skills component and the applied components of the degree.

Core Courses (7 courses): 21 Credits

Statistics Courses

Computer Science Courses

Data Science Courses

Plus Courses and the Culminating Experience (5 courses): 15 Credits

Prerequisites for the Courses Are as Follows:

Sample Schedule of Courses

Admissions

Applications for the program will be reviewed initially by the Graduate Director. A Graduate Committee will be created, comprised of faculty from the participating departments, with responsibility for reviewing all applications meeting the minimum entrance requirements. The general admission requirements for all candidates are:

A bachelor’s degree;
The appropriate background in math and programming, including coursework in Calculus and Analytical Geometry II, Linear Algebra, Introduction to Probability and Statistics, and Introduction to Scientific Programming (Old Westbury courses MA 2320; MA 3160; MA 3210; and CS 2521 or their equivalents);
A minimum cumulative GPA of 3.0 (on a 4.0 scale) or equivalent, in their highest earned degree;
An admission essay;
Two letters of recommendation, at least one from an academic source; and
Submission of scores on the relevant GRE (Graduate Record Examination)* is optional.

International students must meet the following additional criteria: Non-American educational documents evaluated by the American Association of Collegiate Registrars and Admissions Officers (AACRAO) or World Evaluation Services (WES) or Education Credential Evaluators (ECE) or SUNY China Office evaluation service. TOEFL score of 213 computer-based exam or 80 on the Internet version; IELTS score of 6.5 overall band score; iTEP completion of level 4. Conditional Admission with ELS.edu (successful completion of English 112) or Completion of the Advance Level courses at Stony Brook University’s Intensive English Center (IEC).

*Students may be admitted to the program on a provisional basis pending GRE scores, provided a firm date has been set for taking the exam before the end of their first semester. Our target is to have 70 % of students with GRE scores above the following: Verbal 140, Quantitative 140, and Writing 3. These minima will be reviewed annually.

Acceptance decisions are made by the Graduate Committee based upon the submitted application. Any exceptions will be reviewed by the Graduate Director, the Graduate Committee, and the Dean of the School of Arts and Sciences.

Program Policies and Procedures

Schedule of Courses

The College operates on a three semester per year basis: the fall semester begins the end of August and ends in mid-December; the spring semester begins mid-January and ends mid-May; the summer semester has two sessions. The first summer session begins the end of May and ends in early July, and the second summer session begins in early July and ends in mid-August. During the fall and spring semesters, each class meets one evening a week, with the first class period beginning at 5:40 pm and ending at 7:55 pm and the second class period beginning at 8:05 pm and ending at 10:20 pm. During the seven week summer sessions, classes meet two times per week on Mondays and Thursdays with the first class period meeting from 5:30 pm to 7:45 pm and the second class period meeting from 7:55 pm to 10:10 pm.

Academic Advising

All students admitted to the graduate program will be advised by the Director of the Graduate Program. This process entails: full discussion of student goals; explanation of all degree requirements; description of the course schedule and frequency of course offerings over the relevant period; and information about internships and the employment market.

Program Faculty

Ashok Basawapatna
Assistant Professor

Jennie D’Ambroise
Assistant Professor

Glenn Dodd
Visiting Instructor

Maureen Dolan
Associate Professor

Naresh Gupta
Professor

Mohamed Khalefa
Assistant Professor

Myong-Hi Kim
Associate Professor

Yogesh More
Associate Professor

Do-Young Park
Assistant Professor

David Ralston
Assistant Professor

Frank Sanacory
Associate Professor

Geta Techanie
Associate Professor

Nicholas Werner
Assistant Professor

Tong Yi
Senior Lecturer

Lan Zhao
Professor