Data Science, MS

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Adjunct Associate Professor Osuno, Graduate Program Director, 516-463-5554

The program prepares students for advanced careers in data engineering, quantitative research, machine learning, and data analytics. The curriculum includes the mathematical and algorithmic knowledge and skills used in different aspects of data science, such as data science, data mining and visualization, statistical analysis and inference, and machine learning. The program provides a strong foundation in mathematical methods used in data science, including linear algebra, statistical inference, and optimization, as well as a wide range of applied topics in machine learning, data science, and data mining. Students will develop strong data analytics and computational skills. The program consists of 30 semester hours, out of which 18 semester hours are required courses in both math and applied data science courses and either 6 semester hours in elective courses with a master thesis for 6 semester hours or 9 semester hours in elective courses with a capstone project for 3 semester hours. The program can be finished in one academic year for full-time students or two academic years for part-time students.

Admission Requirements

Completion of a bachelor’s degree in mathematics, computer science, or a related discipline from an accredited institution with a minimum 3.0 GPA.
For non-native speakers of English, a TOEFL may be required unless waived by the program director or the Office of Graduate Admission, after having received evidence of English-language proficiency.
Submission of GRE or GMAT scores is optional.

Note: All applications for admission are considered on the basis of their own merits, with weight given to the strength of a student’s previous academic performance, scores obtained on the GRE/GMAT, professional experiences indicating increasing levels of responsibility, and any other pertinent information which the candidate for admission may provide to the Committee on Admissions.

Prerequisite Requirements

Students should satisfy programming and calculus requirements listed below through previous equivalent undergraduate or graduate course work taken within a specific time frame with a B or better grade at an accredited college or university or through satisfactory performance on a proficiency examination administered by the appropriate departments. Applicants may be admitted as provisionally matriculated students if they meet all admission criteria except for the required prerequisites. They may enroll in graduate courses if they meet individual course prerequisites and satisfy the general admission requirements before completing 12 semester hours of graduate study.

Programming Requirements

Programming Principles and Techniques – Any modern programming language will do, but Python or R preferred.

Algorithms and Data Structures – Elementary data structures such as stacks, queues, red-black trees, heaps and hash tables, sorting algorithms, graph algorithms, dynamic programming.

Calculus Requirements

Either Calculus I or II.

Calculus I - Derivatives, the Mean Value Theorem, applications of derivatives in optimization, first and the second derivative affecting the behavior of a function

Calculus II - Definite integrals, anti-derivatives, approximation methods for computation of integrals

Required Courses: 18 Semester Hours

CSC 221 - Data Science Semester Hours: 3
CSC 222 - Data Warehousing Semester Hours: 3
CSC 272 - Machine Learning Semester Hours: 3
MATH 215 - Computational Matrix Algebra and Applications Semester Hours: 3
MATH 216 - Nonlinear Optimization Semester Hours: 3
MATH 245 - Probability and Statistical Inference Semester Hours: 3

Elective Courses: 6 or 9 Semester Hours (See Required Capstone Experience)

Choose elective courses under a chairperson’s advisement from the following lists of computer science or math electives. Courses not listed below could be taken with prior approval from the graduate program director.

CSC 220 - Neural Networks and Deep Learning Semester Hours: 3
CSC 249 - Text Mining Semester Hours: 3
CSC 254 - Database Design Semester Hours: 3
CSC 259 - Concurrent and Parallel Programming Semester Hours: 3
CSC 273 - Data Mining Semester Hours: 3
CSC 284 - Computer Communication Networks and Distributed Processing Semester Hours: 3
MATH 228 - Combinatorial Optimization Semester Hours: 3
MATH 246 - Regression Methods of Data Analysis Semester Hours: 3
CSC 274 - Natural Language Processing Semester Hours: 3

Required Capstone Experience

A capstone project (3 semester hours) or a master thesis (6 semester hours) in data science is required for the completion of this program. If you choose the capstone project, then you need to take an additional elective to complete the required 30 semester hours.

The capstone project is one semester long and it involves an application of data science techniques. It requires a capstone project report and a presentation. For the capstone project the student needs to complete one of the following courses:

CSC 300 - Independent Projects Semester Hours: 3
CSC 303 - Graduate Capstone Project Semester Hours: 3
The master thesis option is for students interested in research in data science and it lasts two semesters. It requires a thesis and a presentation. For the thesis option, the student needs to complete both courses listed below:
CSC 301 - Thesis Background Research Semester Hours: 3
CSC 302 - Thesis Semester Hours: 3
To start either the capstone project or the thesis option, a proposal must be submitted and approved by the project/thesis advisor and the graduate director.

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