Data Science / Master of Science
Program: MS-DATA-1
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.
Time Limitation for Completing the Program
A student must complete all graduation requirements within seven 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 Fast Track 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
| Code | Title | Hours |
|---|---|---|
| Foundation Courses 1 | ||
| CPSC 50100 | Programming Fundamentals | 3 |
| DATA 50000 | Mathematics for Data Scientists | 3 |
| DATA 50100 | Probability and Statistics for Data Scientists | 3 |
| Data Science Core | ||
| DATA 51000 | Data Mining and Analytics | 3 |
| DATA 51100 | Statistical Programming | 3 |
| DATA 51200 | Multivariate Data Analysis | 3 |
| DATA 53000 | Data Visualization | 3 |
| DATA 54000 | Large-Scale Data Storage Systems | 3 |
| DATA 55000 | Supervised Machine Learning | 3 |
| DATA 55100 | Unsupervised Machine Learning | 3 |
| Concentration | ||
| Select one of the following Concentrations: | 12 | |
| Total Hours | 42 | |
- 1
Foundation coursework may be waived for students with sufficient background.
Generalist Track
No concentration
| Code | Title | Hours |
|---|---|---|
| Select three of the following: | 9 | |
| Database Systems | ||
| Pervasive Application Development | ||
| Distributed Computing Systems | ||
| Artificial Intelligence 1 | ||
| Artificial Intelligence 2 | ||
| Natural Language Processing | ||
| Semantic Web | ||
| Digital Image Processing | ||
| Advanced Data Mining and Prescriptive Analytics | ||
| Data Mining for Cyber Security | ||
| Data Engineering | ||
| Masters Project | ||
| DATA 59000 | Data Science Project for Computer Scientists | 3 |
| Total Hours | 12 | |
Cognitive and Prescriptive Analytics
Concentration: CAPA
| Code | Title | Hours |
|---|---|---|
| Concentration Electives | ||
| Select three of the following: | 9 | |
| Artificial Intelligence 1 | ||
| Artificial Intelligence 2 | ||
| Natural Language Processing | ||
| Digital Image Processing | ||
| Advanced Data Mining and Prescriptive Analytics | ||
| Masters Project | ||
| DATA 59000 | Data Science Project for Computer Scientists | 3 |
| Total Hours | 12 | |
Computational Biology and Bioinformatics
Concentration: CBAB
| Code | Title | Hours |
|---|---|---|
| Concentration Courses | ||
| BIOL 50900 | Introduction to Computational Biology | 3 |
| BIOL 51000 | Data Systems in the Life Sciences | 3 |
| BIOL 51200 | Research in Biotechnology | 3 |
| BIOL 59000 | Data Science Project for Life Scientists | 3 |
| Total Hours | 12 | |
Cybersecurity Data Science
Concentration: CSDS
| Code | Title | Hours |
|---|---|---|
| Concentration Courses | ||
| CPSC 50600 | Cyber Security Essentials | 3 |
| CPSC 52500 | Encryption and Authentication | 3 |
| DATA 62500 | Data Mining for Cyber Security | 3 |
| Masters Project | ||
| DATA 59000 | Data Science Project for Computer Scientists | 3 |
| Total Hours | 12 | |
Data Engineering
Concentration: DTEG
| Code | Title | Hours |
|---|---|---|
| Concentration Electives | ||
| Select three of the following: | 9 | |
| Database Systems | ||
| Pervasive Application Development | ||
| Distributed Computing Systems | ||
| Semantic Web | ||
| Data Engineering | ||
| Masters Project | ||
| DATA 59000 | Data Science Project for Computer Scientists | 3 |
| Total Hours | 12 | |
Data Science Research
Concentration: DSRS
Admission to the M.S. in Data Science degree with the Data Science Research Concentration requires an undergraduate minimum G.P.A. of 3.25 and it is recommended that GRE scores be submitted with the application. Students that are denied admission to this concentration can start their program with another concentration, and then apply for a change of concentration. At that time, the Program Director will evaluate their current performance in the program and either approve or decline the application.
Once the student is admitted to the program, they will need to choose a Thesis Advisor among the full-time faculty members of the Engineering, Computing, and Mathematical Sciences (ECaMS) department, who are currently teaching Data Science. This should be done after completing foundation courses and some of the core courses, but before taking the Thesis Research course. Once an advisor is selected, the student will work with that person to form a three-person thesis committee, which will consist of the advisor, one other Data Science faculty member, and one person outside of Data Science. The advisor will guide the student through the course selection process and work with the student on research in the field of Data Science.
The program will require the students to complete a written master's thesis, under the guidance of the advisor. After all course requirements are completed and the master’s thesis is done, the student will need to schedule an oral defense of the thesis in front of the chosen committee. The committee can then decide to pass, fail, or pass conditionally. If the student fails the oral defense, the student will be allowed to attempt to defend the thesis a second time. If that results in failure again, the student cannot graduate in this concentration, but is allowed to change to another concentration. If the student passes conditionally, the conditions must be met before graduation requirements are satisfied. The student will coordinate how the conditions are checked with the thesis committee.
| Code | Title | Hours |
|---|---|---|
| Concentration Electives | ||
| Select two of the following: | 6 | |
| Cyber Security Essentials | ||
| Database Systems | ||
| Pervasive Application Development | ||
| Encryption and Authentication | ||
| Distributed Computing Systems | ||
| Artificial Intelligence 1 | ||
| Artificial Intelligence 2 | ||
| Natural Language Processing | ||
| Digital Image Processing | ||
| Advanced Data Mining and Prescriptive Analytics | ||
| Data Mining for Cyber Security | ||
| Masters Thesis Research | ||
| Students must complete and pass this course two times in two different semesters | ||
| DATA 59500 | Data Science Thesis Research | 6 |
| Total Hours | 12 | |
Full Admission
To be accepted for admission into the program, a student must present the following credentials:
- A baccalaureate degree from a regionally-accredited institution of higher education.
- A minimum undergraduate GPA of 3.0 on a 4.0 scale.
- An application for graduate admission, accompanied by an application fee.
- Professional résumé.
- Official transcripts from all institutions of higher education attended.
- A two-page statement of purpose.
- Two letters of recommendation.
- 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).
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 nine semester hours of graduate study with a GPA of 3.0 or higher. After nine 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.
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 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 nine credit hours applied to the M.S. in Data Science degree, except in the case of an established inter-institutional agreement. Course credits eligible for transfer consideration must meet the following criteria:
- All transfer credit must have been earned prior to matriculation in the M.S. in Data Science program.
- The coursework must have been completed at a regionally-accredited graduate school.
- A minimum grade of B must have been earned for the course.
- The coursework must have an equivalent in the M.S. in Data Science curriculum.
- 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.
- 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 Admission Policies section of this Catalog entitled "Entering International Students."