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. 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. As a curricular initiative, it is especially intriguing because it lends itself naturally to interdisciplinary work with other fields, including other sciences, the social sciences, the humanities, health care, 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.

The Certificates in Computational Biology and Bioinformatics and Data Science consist of a subset of the courses required of the full Master of Science in Data Science. Unlike the M.S. in Data Science, this Certificate does not require the completion of a Data Science Project. The Certificate alternative will provide an intriguing option for students who may not wish to pursue the full graduate degree.

Relationship to Master of Science in Data Science

Students who start out pursuing the graduate certificate option can readily switch to the full degree. A student who completes the coursework for the Certificate in Computational Biology and Bioinformatics as well as courses required for the Master of Science in Data Science will earn both the Certificate and the Master of Science in Data Science.

Contact the Graduate Program Director for master's program policies which are applicable to the certificate program as well. [The undergraduate Fast Track Program option does not apply to applicants for the certificate programs.]

Minimum Requirements for Admission to the Certificate Program

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.
6. A two-page statement of purpose.
7. Two letters of recommendation.
8. Undergraduate mathematics coursework in Calculus¹.

¹

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.

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

Student-At-Large Status

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 the certificate and only courses with grades of B or better will count toward the certificate.

Transfer Admission Procedures

Students may apply up to three semester hours of graduate-level work from other regionally-accredited institutions to their certificate program. The following conditions apply to the acceptance of transfer credit:

1. Only one course with a grade of B or better will be accepted.
2. Coursework must have been completed at a regionally-accredited graduate school.
3. Appropriateness of coursework will be decided by the Graduate Program Director at the time of the student’s application to the program.
4. 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.
5. Transferred credit may not exceed three semester hours in any case.
6. Credit for prior learning is not awarded for graduate programs.

Certificate Requirements

1. A student must earn a minimum of 18 credit hours, but may need to earn up to 24 credit hours depending upon whether the student must take foundation courses. The foundation coursework consists of nine credit hours, but may be waived for students with sufficient background. If a student is waived from the foundation coursework an additional elective course must be taken to meet the minimum 18 credit hours. This elective course can be chosen out of any of the courses that are part of the M.S. in Data Science degree requirements, which are not already required for the certificate.
2. Successful completion of all required courses, including at least 15 credit hours at Lewis University. No more than three credit hours in graduate coursework may be transferred in from a regionally-accredited institution of higher education.
3. Achievement of an overall GPA of 3.0 in all courses taken at Lewis University included in the certificate program.
4. A grade of C- or lower in any course(s) listed below will not apply to the Certificate.