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Data Science, M.S.

The graduate program leading to a Master of Science in Data Science is designed specifically for college graduates holding an appropriate computer science or related degree who wish to pursue a career within the specific subspecialties of Data Science (data analytics, machine learning, visualization, etc.). The program is capable of serving a wide range of professional interests and within this framework takes a practical approach to computer applications.

Program Overview

NYIT’s program is suited for individuals with a baccalaureate degree in computer science, engineering, management, information technology, mathematics, or related fields of interest.

Objectives
Specific objectives of this program provide students with a comprehensive background in:

  1. Fundamental areas of data science such as algorithms, computational theory, analytics, operating systems, compiler design, and machine learning.
  2. Theory and design of modern high-level programming languages and applications in development of data systems.
  3. Design and analysis of efficient algorithms.
  4. Advanced topics in computer architecture, illustrated by case studies from classic and modern processors including large-scale computer systems.

Curriculum
The curriculum consists of 30 credits;15 of which are allocated to required courses in data science. The remaining 15 credits permit students to specialize either in areas appropriate to their individual needs, or to complete the thesis option. In order to accommodate working professionals, courses are offered during day and evening hours, as well as weekends at the Long Island (Old Westbury) and New York City (Manhattan) campuses.

NYIT’s emphasis on real-world, applications-oriented training is ideal for individuals interested in systems analytics, computer networks, systems architecture, data organization, and communications, microprocessors, computer graphics, or artificial intelligence. NYIT graduates will also have the opportunity to receive specialized training in commercialization and entrepreneurship via the Entrepreneurship and Technology Innovation Center (ETIC).

Thesis Option Master’s Degree
Students selecting this option will be required to complete 30 credits, including six credits of M.S. thesis courses and nine credits of general electives. Full-time students typically take two semesters to complete the thesis course sequence, which entails planning and conducting research and writing a thesis. Depending on the thesis topic, students will gain specialized skills and knowledge to make them better qualified for research and development jobs at companies. The thesis may also lead to advanced degrees beyond the Master of Science. With the approval of a supervising thesis advisor, qualified students pursuing the master’s thesis must:

  • Enroll in two semesters of DATA 890 MS Thesis I and DATA 891 MS Thesis II for a maximum of six credits.
  • Prepare reports and verbally defend a formal thesis in accordance with criteria established by the College of Engineering and Computing Sciences. A formal written thesis will be archived in the NYIT library.

Note: All master’s thesis students must strictly adhere to the Master’s Thesis Policies and Guidelines published by NYIT College of Engineering and Computing Sciences.

Non-Thesis Option Master’s Degree
Students selecting this option will still be required to complete 30 credits total, but instead of M.S. thesis courses, students will take twelve elective credits and three project course credits with the department chair’s or advisor’s permission.

Fellowships and Assistantships
Research fellowships and teaching assistantships are available to qualified candidates. These opportunities are usually for a 10-month period and may include partial remission of tuition and fees.

To apply for the M.S. in Data Science, visit nyit.edu/apply.


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Admission Requirements

  • B.S. degree or its equivalent from an accredited college or university in computer science or related area

    • If students have a degree in engineering, an accredited program is one that is accredited by the Engineering Accreditation Commission of the Accreditation Board for Engineering and Technology (ABET).
    • If students have completed degrees in computer science or a closely related field, an accredited program is one taken at a college that is regionally accredited, such as the Middle States Association of Colleges and Schools.
    • If students have an international baccalaureate degree or diploma, which is equivalent to three years of undergraduate study in the U.S. in computer science, engineering, or a related area, they may be eligible to be admitted into a bridge option in the intended graduate program.
  • Minimum undergraduate CGPA in undergraduate studies of 2.85 for full matriculation

    • Applicants who do not qualify for full matriculation and have an undergraduate GPA between 2.5 and 2.84 may, at the discretion of the director, be given the opportunity to demonstrate qualifications for full matriculation by achieving a GPA of 3.0 or higher in the first four graduate courses. In addition, such students may be required to take one or more parts of the GRE and meet individual departmental requirements. In general, students in this category will not be permitted to continue in the program for more than two semesters unless they have qualified for fully matriculated status, or there are special extenuating circumstances.
  • Submit GRE scores

    • Graduates of foreign universities are required to take the GRE and submit their scores.
    • Students with a GPA below 2.85 may, at the discretion of the dean, be asked to take the GRE or other diagnostic tests. Admission will be based upon consideration of test results, previous academic performance, and related employment, if applicable.
  • Students with an insufficient background for admission into the Data Science M.S. program may be admitted if they have satisfied the following prerequisites or equivalents before taking any graduate level courses in the program:

    Waivable Courses

    • CSCI 125 Computer Programming I (3 credits)
    • CSCI 185 Computer Programming II (3 credits)
    • CSCI 235 Elements of Discrete Structures (3 credits)
    • CSCI 260 Data Structures (3 credits)
    • CSCI 270 Probability and Statistics for Computer Science (3 credits)
    • CSCI 300 Database Management (3 credits)
    • CSCI 312 Theory of Computation (3 credits)
    • CSCI 335 Design and Analysis of Algorithms (3 credits)

    Additional Prerequisite Courses

    • MATH 170 Calculus I (4 credits)
    • MATH 180 Calculus II (4 credits)

    Note: Credits earned for the courses above will not be counted toward the 30 credits required for the degree.

Application Materials

  • Completed application
  • $50 nonrefundable application fee
  • Copies of undergraduate transcripts for all schools attended. All final, official transcripts must be received prior to the start of your first semester.
  • Copy of college diploma or proof of degree
  • Official GRE scores, if required (NYIT GRE Code: 2561)
  • International student requirements: English proficiency (TOEFL/IELTS/PTE), I-20, and transcript evaluation

Transfer Credits

  • Students may transfer up to six transfer credits from an accredited graduate program for appropriate courses in which a minimum grade of B was earned.