Data Science, MS

Data Science, Master of Science

Computer Science, Mathematics and Science


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:

  1. 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.
  2. 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.
  3. 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.
  4. Completion of the following undergraduate mathematics course work
    1. Calculus
    2. Probability and Statistics

Office of Graduate Admission

Office of Graduate Admission
[email protected]

Department Contact

Department Faculty
Please see a list of our Computer Science, Mathematics and Science faculty.


Data Analysis/ Applied Statistics, 
(6 credits)

Two of the following:
BUA 602 Business Analytics
BUA 609  Advanced Managerial Statistics
BUA 633  Applied Regression and Forecasting Models


Database Design/ Data Warehousing, Required 
(3 credits)
CUS 510  Database Management Systems
Data Mining/ Predictive Modeling,
(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)
(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
CUS 680   Distributed Big Data Analytics I (pre-requisite CUS 610, CUS 510)
CUS 681   Distributed Big Data Analytics II (pre-requisite CUS 680)
Marketing Analytics
MKT 600 Decisions in Marketing Management
MKT 611  Data Driven Marketing
Healthcare Analytics
HCI 520  Medical and Health Informatics
HCI 525  Applied Healthcare Analytics  (pre-requisite HCI 520; pre/co-requisite CUS 615)
Cyber and Information Security
Choose 2 courses from: CYB 611, CYB 621, CYB 625, CYB 711, CYB 615, DFR 711

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)