Bachelor of Science in
Computational Mathematics
The Bachelor of Science in Computational Mathematics, available at Embry-Riddle's Daytona Beach Campus, provides a solid foundation in the application of mathematics across disciplines that require quantitative analysis. This program develops strong analytical and problem-solving skills related to computing, mathematics, statistics, and basic science.
All majors in the Computational Mathematics program, regardless of track, are encouraged to engage in industrial research opportunities under the guidance of faculty members. Students enrolled in this interdisciplinary major select the Data Science Track or the Engineering Application Track.
Data Science Track
This track is designed for students interested in developing expertise in this significant growth area. Graduates with a data science skill set are in high demand for employment. Using interdisciplinary methods to extract knowledge or insights from large quantities of data, data scientists employ techniques and theories drawn from mathematics, statistics, and computer sciences and apply them to data-rich domains.
This track provides students with a strong background in both the theory and practice of creating knowledge from data that can be used to solve real-world problems.
Engineering Applications Track
This track is designed for students interested in a variety of computational-intensive project-based classes. This track, appealing to students who double major in computational mathematics and an engineering or physics program, assures that graduates are very competitive on the job market. This interdisciplinary track provides students with the fundamentals of mathematical sciences and an introduction to concepts and techniques of computation, optimal decision making, high performance computing, and statistical inference.
DETAILS
About Computational Mathematics at the Daytona Beach, FL Campus
The Computational Mathematics degree, housed in the Department of Mathematics in the College of Arts and Sciences, prepares students for applied mathematics careers that require highly developed critical-thinking and problem-solving skills. This program emphasizes the use of computers as tools to solve mathematically modeled real-world and data-enabled problems.
This program also features project-based learning, as students work on real-world problems provided directly by business and industry. This exceptional experience provides students with many opportunities for internships and undergraduate research, which further bolster employment prospects.
Because of the emphasis on applied mathematics, computing tools, and data-driven applications, the Computational Mathematics degree program provides students with an excellent background to secure entry-level positions in a wide variety of industries including aerospace, engineering, business finance, data analytics, systems analysis, or healthcare.
The Data Science Track of the bachelor's degree in Computational Mathematics offers excellent preparation for students choosing to enroll in the master's degree in Data Science offered at Embry-Riddle.
Program Requirements
| UNIV 101 | College Success | 1 |
| General Education | 31 | |
| Core | 24 | |
| Track | 19 | |
| Electives | 15 | |
| Open Electives | 30 | |
| Total Credits | 120 | |
General Education Requirements
For a full description of Embry-Riddle General Education guidelines, please see the General Education section of this catalog. These minimum requirements are applicable to all degree programs. In addition to completing General Education requirements, BSCM students develop an understanding of the concepts of advanced mathematics with emphasis on the integration of theoretical, practical, and computational viewpoints. By the end of their second year in the program, BSCM students select a track: The Data Science Track or The Engineering Application Track. In addition, BSCM students must also complete a minor or declare a second major that supplements the program core and aligns with their interests and career goals. In the final year of the program, all BSCM students complete a capstone project by applying skills they have acquired in their course work to a real-world problem.
| Communication Theory & Skills (COM 122, COM 219, COM 221) | 9 | |
| Humanities - Lower level | 3 | |
| Social Sciences - Lower level | 3 | |
| Humanities or Social Sciences - Lower or Upper level | 3 | |
| Humanities or Social Sciences - Upper level | 3 | |
| Computer Science (CS 223 or CS 225 or EGR 115) | 3 | |
| Mathematics | 8 | |
| Physical and Life Sciences - one course must include a lab | 7 | |
| Total Credits | 39 | |
BSCM Common Core
| MA 241 | Calculus and Analytical Geometry I | 4 |
| MA 242 | Calculus and Analytical Geometry II | 4 |
| MA 243 | Calculus and Analytical Geometry III | 4 |
| MA 305 | Introduction to Scientific Computing | 3 |
| MA 413 | Statistics | 3 |
| MA 432 | Linear Algebra | 3 |
| MA 490 | Capstone Project | 3 |
| Total Credits | 24 | |
Engineering Application Track
The Engineering Application Track’s curriculum provides a foundation in mathematics through courses in calculus, differential equations, linear algebra, mathematical modeling, numerical analysis, and several other areas. Students develop strong problem-solving, analytical, and programming skills as they work across diverse areas of science and mathematics.
| MA 345 | Differential Equations and Matrix Methods | 4 |
| MA 348 | Numerical Analysis I | 3 |
| MA 441 | Mathematical Methods for Engineering and Physics I | 3 |
| MA 442 | Mathematical Methods for Engineering and Physics II | 3 |
| MA 410 | Linear Optimization | 3 |
| or MA 453 | High Performance Scientific Computing | |
| MA 448 | Numerical Solution of Differential Equations | 3 |
| Total Credits | 19 | |
Data Science Track
Courses within the Data Science track provide BSCM majors with knowledge they need to analyze, understand, and visualize data. Students learn skills to collect, manage, interpret, and analyze data in order to assist in making data-driven decisions. The program includes coursework in areas such as data analytics, mining and visualization with foundational studies in mathematics, statistics, business, and computer science. This track is also a good option for students enrolled in computer science programs to seek a dual major.
| MA 345 | Differential Equations and Matrix Methods | 4 |
| or CS 317 & MA 325 | Files and Database Systems and Matrix Methods | |
| MA 348 | Numerical Analysis I | 3 |
| or MA 360 | Mathematical Modeling & Simulation I | |
| MA 412 | Probability and Statistics | 3 |
| or MA 404 | Statistics and Research Methods | |
| MA 410 | Linear Optimization | 3 |
| DS 440 | Data Mining | 3 |
| DS 444 | Scientific Visualization | 3 |
| Total Credits | 19 | |
* Elective requirement may be met with declaration and completion of any MINOR or TWO DEGREES OF THE SAME RANK or DOUBLE MAJOR. (ROTC courses also acceptable)
| Year One | ||
|---|---|---|
| Credits | ||
| COM 122 | English Composition | 3 |
| COM 219 | Speech | 3 |
| EGR 115 | Introduction to Computing for Engineers | 3 |
or CS 223
|
Scientific Programming in C | |
or CS 225
|
Computer Science II | |
| MA 241 | Calculus and Analytical Geometry I | 4 |
| MA 242 | Calculus and Analytical Geometry II | 4 |
| Physical Science Elective | 3 | |
| UNIV 101 | College Success | 1 |
| HU Lower Level Elective | 3 | |
| SS Lower Level Elective | 3 | |
| Open Electives | 3 | |
| Credits Subtotal | 30.0 | |
| Year Two | ||
| COM 221 | Technical Report Writing | 3 |
| MA 243 | Calculus and Analytical Geometry III | 4 |
| MA 305 | Introduction to Scientific Computing | 3 |
| MA 325 | Matrix Methods | 1 |
| MA 412 | Probability and Statistics | 3 |
| Physical Science Elective | 3 | |
| Physical Science Laboratory | 1 | |
| Elective * | 3 | |
| Lower or Upper-Level Humanities or Social Science Elective | 3 | |
| Open Elective | 6 | |
| Credits Subtotal | 30.0 | |
| Year Three | ||
| CS 317 | Files and Database Systems | 3 |
| DS 440 | Data Mining | 3 |
| MA 360 | Mathematical Modeling & Simulation I | 3 |
| MA 413 | Statistics | 3 |
| MA 432 | Linear Algebra | 3 |
| Upper Level Humanities or Social Science Elective | 3 | |
| Elective * | 6 | |
| Open Electives | 6 | |
| Credits Subtotal | 30.0 | |
| Year Four | ||
| DS 444 | Scientific Visualization | 3 |
| MA 410 | Linear Optimization | 3 |
| MA 490 | Capstone Project | 3 |
| Elective * | 6 | |
| Open Electives | 15 | |
| Credits Subtotal | 30.0 | |
| Credits Total: | 120.0 | |
| Year One | ||
|---|---|---|
| Credits | ||
| COM 122 | English Composition | 3 |
| COM 219 | Speech | 3 |
| EGR 115 | Introduction to Computing for Engineers | 3 |
or CS 223
|
Scientific Programming in C | |
or CS 225
|
Computer Science II | |
| MA 241 | Calculus and Analytical Geometry I | 4 |
| MA 242 | Calculus and Analytical Geometry II | 4 |
| Physical Science Elective | 3 | |
| UNIV 101 | College Success | 1 |
| HU Lower Level Elective | 3 | |
| SS Lower Level Elective | 3 | |
| Open Electives | 3 | |
| Credits Subtotal | 30.0 | |
| Year Two | ||
| COM 221 | Technical Report Writing | 3 |
| MA 243 | Calculus and Analytical Geometry III | 4 |
| MA 305 | Introduction to Scientific Computing | 3 |
| MA 345 | Differential Equations and Matrix Methods | 4 |
| MA 413 | Statistics | 3 |
| Physical Science Elective | 3 | |
| Physical Science Laboratory | 1 | |
| Elective * | 3 | |
| Lower or Upper-Level Humanities or Social Science Elective | 3 | |
| Open Elective | 3 | |
| Credits Subtotal | 30.0 | |
| Year Three | ||
| MA 348 | Numerical Analysis I | 3 |
| MA 432 | Linear Algebra | 3 |
| MA 441 | Mathematical Methods for Engineering and Physics I | 3 |
| MA 442 | Mathematical Methods for Engineering and Physics II | 3 |
| Elective * | 6 | |
| Upper Level Humanities or Social Science Elective | 3 | |
| Open Electives | 9 | |
| Credits Subtotal | 30.0 | |
| Year Four | ||
| MA 448 | Numerical Solution of Differential Equations | 3 |
| MA 453 | High Performance Scientific Computing | 3 |
| MA 490 | Capstone Project | 3 |
| Elective * | 6 | |
| Open Electives | 15 | |
| Credits Subtotal | 30.0 | |
| Credits Total: | 120.0 | |
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