Data Science / Master of Science

The Master of Science in Data Science (MSDS) program provides students with background and insights into the key mathematical and computer science issues involved in the analysis of massive data sets. Professionals with this expertise can work on behalf of organizations that collect tremendous quantities of data.  These organizations employ data scientists to develop effective solutions for distilling meaning from what would otherwise be a disorganized bundle of seemingly disconnected data points. Data Scientists specialize in developing solutions for organizations to accomplish this goal.

Data Science is a field of study within Computer Science that explores how large quantities of data can be efficiently stored, managed, queried, and summarized, and how massive data sets can be used for making predictions.  It uses mathematical theory and techniques from probability, statistics, linear algebra, and modeling, along with computer science concepts and skills in distributed storage, distributed processing, networks, security, human-machine interfaces, software development, and algorithms to develop software and systems that enable consumers of Big Data to identify critical data assets and interpret them. This field is inherently interdisciplinary, as the skills and concepts of data science are applied in the natural sciences, the social sciences, the humanities, healthcare, business, and education. Data scientists seem to play at the center of a new renaissance. The field must therefore be studied both for its inherent scientific and mathematical richness as well as for its immediate, specific application to diverse fields.

Experts in data science can find employment in a wide variety of industries and organizations, as virtually every enterprise can benefit from solutions that use data mining and analytics techniques. This program aims to prepare specialists who can develop software and hardware systems that manage large data sets and deploy them for solving solutions in specific disciplines. To emphasize application, students are required to pursue a concentration in a specific discipline where they apply the concepts and techniques of data science to contemporary problems in particular application areas. Each concentration consists of a minimum of 12 credit hours of coursework.

Full Admission

To be accepted for admission into the program, a student must present the following credentials:

1. A baccalaureate degree from a regionally-accredited institution of higher education.

2. A minimum undergraduate GPA of 3.0 on a 4.0 scale.

3. An application for graduate admission, accompanied by an application fee.

4. Professional résumé.

5. Official transcripts from all institutions of higher education attended.

7. A two-page statement of purpose.

8. Two letters of recommendation.

9.  Undergraduate mathematics coursework in Calculus*.

Please note: International students are required to have a TOEFL test score greater than 550 (computer-based 213; Internet-based 79). 

*With regard to the Calculus requirement, note that intimate, immediate familiarity with Calculus is not expected, but students should have worked with integrals and derivatives at some point in their academic preparation.

Provisional Admission

Under certain circumstances, students who do not meet the GPA requirement (GPA below 3.0, but above 2.5) for full admission may request to be admitted to the program on a provisional basis. Provisionally-admitted students must complete the first 9 semester hours of graduate study with a GPA of 3.0 or higher. After 9 hours of completed coursework, a provisionally-accepted student’s application will be reviewed again for full admission. This decision will be made by the Graduate Program Director in consultation with the Graduate Council of the College of Aviation, Science, and Technology.



A student-at-large is not a degree candidate. In order to be admitted as a student-at-large, the applicant must submit official documentation of a baccalaureate degree from a regionally-accredited institution of higher education and complete a modified application form. The decision to admit an at-large student to graduate courses belongs to the Graduate Program Director, whose decision is based on an evaluation of the applicant’s undergraduate coursework and possibly an interview. However, should the student decide to apply for full admission status at a later time, but within five years of course completion, only a maximum of nine semester hours of graduate coursework completed as a student-at-large can be applied toward an advanced degree, and only courses with grades of B or better will count toward the degree.


Transfer of Graduate Credit 

A student entering the M.S. in Data Science program with appropriate prior graduate coursework in data science may have a maximum of 9 credit hours applied to the M.S. in Data Science degree.  Course credits eligible for transfer consideration must meet the following criteria: 

1. All transfer credit must have been earned prior to matriculation in the M.S. in Data Science program.

2. The coursework must have been completed at a regionally-accredited graduate school.

3. A minimum grade of B must have been earned for the course.

4. The coursework must have an equivalent in the M.S. in Data Science curriculum.

5. Courses from outside the United States will be considered if they are evaluated as graduate level by the Office of Admission or the Commission on Accreditation of the American Council on Education.

6. Credit for prior learning is not awarded for graduate courses.

International Students

International students are required to meet all the admission requirements for full or provisional admission and also the admission requirements specified in the General Information section of this Catalog entitled "Entering International Students."

Good Academic Standing

See Good Academic Standing.

Academic Probation and Dismissal

See Academic Probation and Dismissal.

Enrollment of Undergraduates in Graduate Courses

See Undergraduate Students Enrolling in Graduate Courses.

Time Limitation for Completing the Program

A student must complete all graduation requirements within 7 years from completion of the first graduate course taken at Lewis University.  Students remain under the requirement of the catalog in effect at the time of matriculation unless they discontinue attendance for two consecutive years or more, in which case they will follow the catalog in effect upon their return.

Certificate in Data Science

An applicant who wishes to pursue a graduate Certificate in Data Science or Computational Biology and Bioinformatics must meet the requirements for full admission to the MSDS program. If the student decides later to switch from the certificate program to the master's program, all courses that satisfy the requirements of the certificate will apply to the master's. A course grade of C- or lower will not satisfy the requirements of either the certificate or the master's. [The 4+1 Program option for undergraduates is not available to applicants for the certificate.]

Graduation Requirements

To complete the M.S. in Data Science degree, a student must earn a minimum of 33 credit hours, but may need up to 42 credit hours depending on whether the student must take foundation courses. The foundation coursework consists of nine credit hours, but may be waived for students with sufficient background. The core curriculum for the degree consists of 21 credit hours, and the concentrations require at least 12 additional credit hours in a specific application of data science.


Degree Offered:  Master of Science

Total Credit Hours:  33-42


Degree Requirements

Program: MS-DATA-1

Foundation Coursework (9)

CPSC-50100Programming Fundamentals


DATA-50000Mathematics for Data Scientists


DATA-50100Probability and Statistics for Data Scientists


Data Science Core (21)

DATA-51000Data Mining and Analytics


DATA-51100Statistical Programming


DATA-51200Multivariate Data Analysis


DATA-53000Data Visualization


DATA-54000Large-Scale Data Storage Systems


DATA-55000Supervised Machine Learning


DATA-55100Unsupervised Machine Learning


Computer Science Concentration (12)

Concentration: CPSC

Electives (9)

Take three of the following courses:
CPSC-50600Cyber Security Essentials


CPSC-51700Pervasive Application Development


CPSC-52500Encryption and Authentication


CPSC-55200Semantic Web


CPSC-55500Distributed Computing Systems


CPSC-57100Artificial Intelligence 1


CPSC-57200Artificial Intelligence 2


CPSC-57400Natural Language Processing


DATA-56600Digital Image Processing


Master's Project (3)

DATA-59000Data Science Project for Computer Scientists


Computational Biology and Bioinformatics Concentration (12)

Concentration: CBAB
BIOL-50900Introduction to Computational Biology


BIOL-51000Data Systems in the Life Sciences


BIOL-51200Research in Biotechnology


BIOL-59000Data Science Project for Life Scientists