Data Science and Statistics, Bachelor of Science (B.S.) & Applied Mathematics, Master of Arts (M.A.) Accelerated 3+2 Dual Degree Program
Students accepted to the 3+2 Accelerated Dual Degree Option are able to complete their BS degree and MS degree within 5 calendar years because of the accelerated curriculum; nine semester hours of graduate coursework will apply to both the undergraduate BS degree and the graduate MS degree. Only undergraduate students of proven academic ability will be considered for the program. Students should be aware that, in order to maintain their progress in the accelerated 3+2 program, careful coordination with their advisor is required. Depending upon undergraduate progress at the time of 3+2 admission, some summer school classes may be needed.
Admission Requirements for the 3+2 Program:
Students interested in this program must satisfy all the following conditions:
1. Have Junior or Senior standing
2. Have an overall grade point average (GPA) of at least 3.0 at the time of admission to the 3+2 program
3. Be approved by both the Department of Mathematics and Statistics and the Graduate School (see the 3+2 Enrollment Approval Form at http://gradschool.eku.edu/graduate-school-forms)
4. Maintain an overall undergraduate and graduate GPA of at least 3.0 to continue each semester with 3+2 coursework
5. Have an institutional undergraduate and graduate GPA of at least 3.0 to be allowed to move into graduate student status after earning the B.S. Data Science and Statistics degree.
Program Requirements
CIP Code: 27.0501
Students in the 3+2 Accelerated Dual Degree Option must complete the Data Science and Statistics (B.S.) program requirements listed below, with at least a 3.0 GPA, and must apply and be approved to graduate with that degree before being admitted as a graduate student and allowed to proceed to the M.A. in Applied Mathematics program. Nine credit hours of graduate coursework (MAT/STA 720, STA 775, and MAT 865) will be applicable to the undergraduate degree.
Upon successful completion of this program, the graduate will: (1) understand the applications and use of data science and in data science or statistics in everyday life; (2) be able to apply a wide variety of statistical techniques; (3) be able to analyze large, complex data sets; (4) use computer packages to perform statistical analyses; (5) be well qualified for employment in industry, government, and the actuarial profession; and (6) be prepared to pursue graduate work in data science or statistics.
Summary Checklist for General Education
Code | Title | Hours |
---|---|---|
Element 1 | ||
A: Written Communication | 3 | |
B: Written Communication | 3 | |
C: Oral Communication | 3 | |
Element 2 | ||
Quantitative Reasoning | 3 | |
Element 3 | ||
A: Arts | 3 | |
B: Humanities | 3 | |
Element 4 | ||
Natural Sciences | 6 | |
Element 5 | ||
A: Historical Science | 3 | |
B: Social Behavioral Science | 3 | |
Element 6 | ||
Diversity of Perspectives Experiences | 6 | |
Total Hours | 36 |
Students are expected to complete Elements 1 and 2 within their first 60 hours of college credit.
Major
Only courses completed with a grade of at least a “C” will count toward the major requirements.
Code | Title | Hours |
---|---|---|
University Graduation Requirements | ||
General Education | 36 | |
Student Success Seminar | ||
SCO 100 | Student Success Seminar | 1 |
Upper division courses (42 hours distributed throughout Major/Supporting/Gen Ed/Free Electives categories) | ||
Major Requirements | ||
Core Courses | ||
MAT 239 | Linear Algebra and Matrices | 3 |
MAT 244 | Calculus II | 4 |
STA 270 | Applied Statistics | 4 |
STA 340 | Applied Regression Analysis | 3 |
STA 498W | Statistics Capstone | 3 |
MAT 720 | Mathematical Statistics I | 3 |
or STA 720 | Mathematical Statistics I | |
STA 775 | Statistics Methods Using SAS | 3 |
MAT 865 | Applied Linear Algebra | 3 |
Choose from three hours of CSC, DSC, MAT, STA courses numbered 300 or above ^{3} | 3 | |
Major Electives | ||
Choose from one of the following combinations: ^{4} | 6 | |
Data Science: | ||
Data Structures and Programming and Machine Learning | ||
Discrete Mathematics: | ||
Discrete Mathematics and Applied Probability | ||
Statistics: | ||
Mathematical Statistics II ^{2} | ||
Experimental Design | ||
Supporting Course Requirements | ||
Choose from one of the following: | 3 | |
Intro to Game Programming | ||
Introduction to Programming for Science & Engineering | ||
Computing Concepts and Programming | ||
Object- Oriented Programming I | ||
ENG 300 | Introduction to Technical and Professional Writing | 3 |
or ENG 300S | Intro to Tech/Prof Writing | |
MAT 234 | Calculus I (Element 2) ^{G,5} | 4 |
Choose from one of the following: | 0-3 | |
Beginning Ethics (Element 3B) ^{G} | ||
Beginning Ethics (Element 3B) ^{G} | ||
Technology and Values | ||
Domain Knowledge Component | ||
Choose two courses from one of the following categories: | 6-7 | |
Anthropology and Sociology: | ||
Primate Ecology & Sociality | ||
Social Statistics | ||
Population and Society | ||
Research Methods in Sociology | ||
Biology and Environmental Health Sciences: | ||
One Health: Global Environmental Public Health and Environmental Disease Detectives: Epidemiology | ||
Genetics and Bioinformatics: Principles and Applications ^{2} | ||
Ecology and Conservation Biology ^{2} | ||
Computer Information Systems: | ||
Data Base Management ^{2} | ||
Business Data Mining | ||
or BUS 304 | Essentials of MIS | |
Computer Science and Informatics: | ||
Data Structures ^{2} | ||
Database Systems ^{2} | ||
MS Office & Data Analysis ^{2} | ||
Government: | ||
Research and Writing in Political Science ^{2} | ||
Capstone Course in Political Science ^{2} | ||
Public Opinion & Voting Behavior | ||
Geosciences: | ||
Geoscience Data and Techniques ^{2} | ||
Geographic Information Systems | ||
Advanced GIS | ||
Remote Sensing | ||
Advanced Geographic Imagery | ||
Physics: | ||
Electrical Circuits ^{2} | ||
PHY 406 | ^{2} | |
Classical Mechanics ^{2} | ||
Psychology: | ||
Scientific Literacy in Psychology ^{2} | ||
Sensation and Perception | ||
or PSY 315L | Sensation and Perception Lab | |
Research Literacy in Psychology | ||
Tests and Measurements | ||
Advisor-Approved: | ||
Two advisor-approved courses from a department other than the Department of Mathematics and Statistics | ||
Free Electives | ||
Choose from 32-35 hours of free electives | 32-35 | |
Total Hours | 120 |
- ^{ 1 }
Must include at least one of DSC 580 R and Introductory Data Mining or STA 575 Statistical Methods Using SAS or STA 580 R and Introductory Data Mining
- ^{ 2 }
Requires a pre-requisite course
- ^{ 3 }
Excluding: any 349 courses, MAT 303 Mathematical Models and Applications, STA 500 . STA 480 Seminar in ___ will count for only approved topics.
- ^{ 4 }
Courses will not count in both the Core and Major Electives categories.
- ^{ 5 }
Three hours count toward Element 2^{G}
- G
Course also satisfies a General Education element. Hours are included within the 36 hours in General Education.
Applied Mathematics, Master of Arts (M.A.)
See Applied Mathematics, Master of Arts with a Concentration in Applied Mathematics and Statistics (M.A.), Applied Mathematics, Master of Arts with a Concentration in Data Science (M.A.), or Applied Mathematics, Master of Arts with a Concentration in Secondary Mathematics (M.A.)