
The Lesley H. and William L. Collins College of Professional Studies
Queens Campus
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- Data Science, Master of Science
Overview
The M.S. in Data Science prepares students for related careers that involve the application of computational and statistical techniques that are becoming more vital to industry and research. This is accomplished through coursework in topics such as database management systems, data mining and machine learning algorithms, data visualization, statistics, text analytics, and big data. Graduates of the Data Science program will obtain a variety of skills required to analyze large datasets and to develop modeling solutions to support decision making. They will also develop a specialization in distributed Big Data, marketing analytics, healthcare analytics, or cyber and information security. As a STEM approved program, international students are eligible for the STEM OPT extension.
Data Science applies powerful statistical and computational techniques to large data sets in order to generate useful information, identify patterns and trends, and build predictive models. Applications of these techniques are now transforming decision-making throughout business, finance, marketing, government, healthcare, and science. The demand for professionals knowledgeable in this area is projected to grow rapidly in the coming years.
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Admissions Criteria
Admission to the program is contingent upon an assessment of the candidate’s ability to successfully pursue graduate study. This assessment will be made by examining previous academic performance, letters of recommendation, the applicant’s essay, work experience, and any other evidence that the admissions committee believes to be relevant.
Applicants must submit the following evidence of their ability to pursue graduate study:
- A baccalaureate degree from a regionally accredited college or university. Transcripts from each institution attended must be submitted even if a degree was not conferred.
- A record of scholarly achievement at the undergraduate level. Applicants are expected to have a 3.0 (based on a 4.0 scale) cumulative undergraduate grade point average, and a 3.0 in their major field of study. An applicant whose grade point average is below 3.0 may submit an official copy of his/her GRE to support his or her application.
- Two letters of recommendation from individuals who can comment on the applicant’s academic abilities and potential to succeed in an academically rigorous graduate program. At least one of these letters must be from an instructor who has taught and evaluated the applicant in an academic setting.
- Completion of the following undergraduate mathematics course work
- Calculus
- Probability and Statistics
Contact
Office of Graduate Admission
Office of Graduate Admission
718-990-1601
[email protected]
Department Contact

Dr. Christina Schweikert
Program Coordinator718-990-7439
[email protected]
Department Faculty
Please see a list of our Computer Science, Mathematics and Science faculty.
Courses
Courses | |
Data Analysis/ Applied Statistics, Required (6 credits) | Two of the following:
|
Database Design/ Data Warehousing, Required (3 credits) | CUS 510 Database Management Systems |
Data Mining/ Predictive Modeling, Required (6 credits) | CUS 610 Data Mining and Predictive Modeling I (pre/co-requisite CUS 510) CUS 615 Data Mining and Predictive Modeling II (pre-requisite CUS 610) |
Electives (6 credits) | Choose 2 elective courses from: CUS 620 Introduction to Programming for Analytics CUS 625 Data Visualization(pre/co-requisite CUS 610) CUS 635 Web Data Mining (pre/co-requisite CUS 610, CUS 620) CUS 640 Natural Language Processing CUS 675 Database Programming (prerequisite CUS 1126; pre/co-requisite CUS 610) |
Specialization, Required (6 credits) Choose either: Big Data Analytics, Marketing Analytics, Healthcare Analytics or Cyber and Information Security | Big Data Analytics |
Capstone Course Required (3 credits) | Choose 1 capstone course from CUS 690 Applied Analytics Project (pre/co-requisite CUS 615) CUS 695 Software Implementation Project (pre-requisite CUS 1126; pre/co-requisite CUS 615) |
Total (30 credits) |