{"id":330995,"description_type":{"id":3,"name":"Full Catalog Description"},"description":"<p>The Industrial Mathematics track in the Mathematical Science MS program prepares graduate students to pursue careers in industry by providing them with high-quality professional training in branches of mathematics valuable to high-technology industries. This track has three components: training in the necessary mathematics to pursue a career in industrial mathematics, professional training to prepare for the environment of the industrial workplace, and a required experiential component.</p><p><strong>Total Credit Hours Required: 30 Credit Hours Minimum beyond the Bachelor's Degree</strong></p><h2>Track Prerequisites</h2><p>The following courses are required as prerequisites to this track: Calculus with Analytic Geometry I, II, and III; Differential Equations; Linear Algebra; proficiency in a computer language (Python, MATLAB, or C); maturity in the language of advanced calculus (at the level of MAA 4226), and Statistics.</p><h2>Degree Requirements</h2><h3>Required Courses</h3> 12 Total Credits <ul><li>Complete the following: <ul><li>MAA5237 - Mathematical Analysis (3)</li><li>MAS5145 - Advanced Linear Algebra and Matrix Theory (3)</li><li>MAT5712 - Scientific Computing (3)</li><li>MAP6385 - Applied Numerical Mathematics (3)</li></ul></li></ul><h3>Restricted Electives</h3> 12 Total Credits <ul><li>Complete at least 4 of the following: <ul><li>MAA5238 - Measure and Probability I (3)</li><li>MAA6245 - Measure and Probability II (3)</li><li>MAP6111 - Mathematical Statistics (3)</li><li>MAP5336 - Ordinary Differential Equations and Applications (3)</li><li>MAP6356 - Partial Differential Equations (3)</li><li>MAA6405 - Complex Analysis I (3)</li><li>MAA6306 - Real Analysis (3)</li><li>MAA6506 - Functional Analysis (3)</li><li>MAA7239 - Asymptotic Methods in Mathematical Statistics (3)</li><li>MAD5205 - Graph Theory I (3)</li><li>MAD6309 - Graph Theory II (3)</li><li>MAP7386 - Numerical Solutions of PDE (3)</li><li>MAP6118 - Introduction to Nonlinear Dynamics (3)</li><li>MAP6195 - Mathematical Foundations for Massive Data Modeling and Analysis (3)</li><li>MAP6197 - Mathematical Introduction to Deep Learning (3)</li><li>MAP6207 - Optimization Theory (3)</li><li>MAP6218 - Stochastic Calculus (3)</li><li>MAP6387 - Numerical Linear Algebra (3)</li><li>MAP6416 - Applied and Computational Harmonic Analysis (3)</li><li>MAP6445 - Approximation Techniques (3)</li><li>MAP6469 - Bayesian Analysis and Approximation Theory (3)</li><li>MAP7359 - Advanced Topics in Partial Differential Equations (3)</li></ul></li></ul><h3>Professional Development Restricted Electives and Internship</h3> 6 Total Credits <ul><li>Complete 1 of the following<ul>Option 1<li>Complete at least 2 of the following: <ul><li>ENT6016 - New Venture Design (3)</li><li>ENT6617 - Entrepreneurship in Established Organizations (3)</li><li>ENT6946 - Small Business Consulting (3)</li><li>MAN6245 - Organizational Behavior and Development (3)</li></ul></li> Option 2<li>Complete all of the following<ul><li>Complete at least 1 of the following: <ul><li>ENT6016 - New Venture Design (3)</li><li>ENT6617 - Entrepreneurship in Established Organizations (3)</li><li>ENT6946 - Small Business Consulting (3)</li><li>MAN6245 - Organizational Behavior and Development (3)</li></ul></li><li>Earn at least 3 credits from the following types of courses: A student takes an industrial internship (MAP 6946) with satisfactory completion, or takes the seminar sequence: MAP5931 -Proseminar for Financial Mathematics (1) and MAP 5933 -Seminar in Financial Mathematics (2). </li></ul></li> Option 3<li>Complete all of the following<ul><li>Complete at least 1 of the following: <ul><li>ENT6016 - New Venture Design (3)</li><li>ENT6617 - Entrepreneurship in Established Organizations (3)</li><li>ENT6946 - Small Business Consulting (3)</li><li>MAN6245 - Organizational Behavior and Development (3)</li></ul></li><li>Complete at least 1 of the following: <ul><li>MAP5117 - Mathematical Modeling (3)</li><li>MAP6197 - Mathematical Introduction to Deep Learning (3)</li><li>MAP6195 - Mathematical Foundations for Massive Data Modeling and Analysis (3)</li></ul></li></ul></li></ul></li></ul><h4>Grand Total Credits: <strong>30</strong></h4><h2>Application Requirements</h2><h2>Financial Information</h2><h2>Fellowship Information</h2>The program consists of 30 credit hours of courses and internship. Students will work with an adviser to design a program of study, which will be presented to the program director for approval. If a student has an industrial sponsor, the student's program of study will be developed in consultation with a representative from their sponsoring company. Students are expected to obtain hands-on experience. The capstone requirement for this track is fulfilled by students completing an experiential learning requirement (3 credit hours). At least one-half of the program courses must be taken at the 6000 level.","primary":false,"program":1310}