Data Science / Master of Science
Overview
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 one or more of the requirements for full admission may be admitted to the program on a provisional basis. Provisional admission is most commonly offered to applicants who earned an undergraduate GPA less than 3.0 (but greater than 2.5). Students granted provisional status will be considered for full admission by the Graduate Program Director in consultation with the Graduate Council of the College of Arts and Sciences only after they have attempted 9 credit hours in the program and earned a GPA of 3.0 out of 4.0. Provisionally-admitted students who have not attained a minimum GPA of 3.0 out of 4.0 after attempting 9 credit hours of the M.S. in Data Science coursework will be dismissed from the program.
Student-At-Large
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 5 years of course completion, only a maximum of 9 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.
[Students designated by the Computer and Mathematical Sciences Department as having been participants in good standing in the undergraduate Fast Track program offered by Lewis University and who have fulfilled all the requirements for Fast Track delineated in the undergraduate catalog may apply up to 9 credits towards the Master of Science in Data Science.]
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."
Grade Point Requirement
A minimum cumulative GPA of 3.0 is required for graduation. Only grades attained in Lewis University graduate courses will be used to determine the GPA. A grade of D will not count toward degree requirements. Any student admitted to the M.S. in Data Science program whose GPA falls below 3.0 will be placed on academic probation.
Academic Probation
Any student admitted to the M.S. in Data Science program whose GPA falls below 3.0 will be placed on academic probation. While on academic probation, the student must achieve a GPA of 3.0 or better in the courses taken during each 8-week session. If a student does not meet this minimum GPA requirement in the courses taken during any session on academic probation, he or she will be dismissed from the program. After a one-session hiatus, the student may petition the Graduate Program Director in writing to resume studies. The Graduate Program Director, in consultation with the Graduate Council, will make the final decision on whether to allow the student to resume studies. If consent is granted to resume studies in the M.S. in Data Science program, the student will resume studies on academic probation. Once a student’s GPA meets or exceeds 3.0, the student will be released from academic probation. A minimum cumulative GPA of 3.0 is required to graduate.
Enrollment of Undergraduates in Graduate Courses
Registration by undergraduates in graduate courses is limited to a maximum of 2 courses. The student must be within 30 credits of completing the bachelor’s degree requirements, have at least a 3.0 GPA, and have the approval of the Graduate Program Director. Registration for graduate courses will be included in the student’s undergraduate registration form.
Credit earned in a graduate course may be considered as either graduate credit (and not to be counted toward the undergraduate degree) or as undergraduate credit (to be counted in the number of credits required for the baccalaureate degree). Credit earned in a graduate course may not be counted toward more than one degree. [However, Lewis University undergraduates accepted into the Computer and Mathematical Sciences Department's Fast Track program may apply up to 9 graduate credit hours to both graduate and undergraduate degrees. These students must follow the guidelines published in the undergraduate catalog.]
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 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 D will not satisfy the requirements of either the certificate or the master's. [The Fast Track 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 36 credit hours, depending on the concentration the student decides to pursue. The core curriculum for the degree consists of 24 credit hours, and the concentrations require at least 12 additional credit hours in a specific application of data science. A student may pursue only one concentration.
Degree Offered: Master of Science
Total Credit Hours: 36
Curriculum
Data Science Core (24)
| 13-510 | Mathematics for Data Scientists | 3 |
| 13-511 | Concepts of Statistics I | 3 |
| 70-510 | Introduction to Data Mining and Analytics | 3 |
| 70-511 | Statistical Programming | 3 |
| 70-525/68-525 | Encryption and Authentication | 3 |
| 70-530 | Data Visualization | 3 |
| 70-540 | Large-Scale Data Storage Systems | 3 |
| 70-550 | Machine Learning | 3 |
Concentration for Computer Scientists (12)
| 70-590 | Data Science Project for Computer Scientists | 3 |
| 13-512 | Concepts of Statistics II | 3 |
| 70-517 | Pervasive Application Development | 3 |
| 70-552 | Semantic Web | 3 |
| 70-555 | Distributed Computing Systems | 3 |
Concentration: Computational Biology and Bioinformatics(12)
Formerly known as Concentration for Life Scientists.
| 02-509 | Introduction to Computational Biology | 3 |
| 02-510 | Data Systems in the Life Sciences | 3 |
| 02-512 | Research in Biotechnology | 3 |
| 02-590 | Data Science Project for Life Scientists | 3 |