| Core Curriculum Courses | ||
| See the Core Curriculum Courses | 42 | |
| Required Courses, 31 credit hours: | ||
| MATH 2305 | Discrete Mathematics | 3 |
| MATH 2318 | Linear Algebra | 3 |
| MATH 2320 | Differential Equations | 3 |
| Calculus I * | ||
| MATH 2414 | Calculus II | 4 |
| MATH 2415 | Calculus III | 4 |
| MATH 402 | Mathematical Statistics I | 3 |
| MATH 403 | Mathematical Statistics II | 3 |
| COSC 1436 | Introduction to Computer Science and Programming | 4 |
| COSC 1437 | Programming Fundamentals II | 4 |
| or PHYS 2425 | University Physics I | |
| or PHYS 2426 | University Physics II | |
| Math Concentration Courses, 9 credit hours: | ||
| MATH 334 | Abstract Algebra | 3 |
| MATH 436 | Real Analysis | 3 |
| or MATH 438 | Complex Analysis | |
| or MATH 440 | Topology | |
| MATH 437 | Number Theory | 3 |
| Math Concentration Electives, 6 credit hours: | ||
| In addition to the required courses, 6 hours of Math courses at 300/400 level except MATH 301, 361, 362, 371, 372, 380 | 6 | |
| Additional Electives, 32 credit hours: | ||
| A minor or second major is strongly suggested to meet the academic and career interests. Meet a math faculty member to choose a minor or a second major. | 32 | |
| Total Hours | 120 | |
| Core Curriculum Courses | ||
| See the Core Curriculum Requirements | 42 | |
| Required courses in the major, 31 credit hours: | ||
| MATH 2305 | Discrete Mathematics | 3 |
| MATH 2318 | Linear Algebra | 3 |
| MATH 2320 | Differential Equations | 3 |
| Calculus I * | ||
| MATH 2414 | Calculus II | 4 |
| MATH 2415 | Calculus III | 4 |
| MATH 402 | Mathematical Statistics I | 3 |
| MATH 403 | Mathematical Statistics II | 3 |
| COSC 1436 | Introduction to Computer Science and Programming | 4 |
| COSC 1437 | Programming Fundamentals II | 4 |
| or PHYS 2425 | University Physics I | |
| Data Science Concentration Courses, 24 credit hours: | ||
| COSC 2336 | Data Structures and Algorithms | 3 |
| CSCI 333 | Applied Data Analytics with Python | 3 |
| CSCI 340 | Database | 3 |
| CSCI 334 | Introduction to Statistical Programming Methods with R | 3 |
| MATH 303 | Introduction to Data Science | 3 |
| MATH 341 | Linear Regression Models | 3 |
| MATH 342 | Mathematics for Machine Learning | 3 |
| MATH 486 | Image Processing with Applications | 3 |
| Data Science Concentration Electives, 6 credit hours: | ||
| 6 hours of math or approved second major courses at 300/400 level except MATH 301, 361, 362, 371, 372, 380 | 6 | |
| Additional Electives, 17 credit hours: | ||
| Meet a math faculty member for taking electives. | 17 | |
| Total Hours | 120 | |
This courses will satisfy the Core Curriculum Requirements
