Faculty: Engineering The Mathematical Engineering major integrates advanced mathematical principles with engineering applications to solve complex real-world problems. Students gain skills in applied mathematics, numerical analysis, optimization, statistical modeling, and computational methods, preparing for careers in various engineering fields, data science, finance, and research institutions. **Learning Objectives:** - Understand advanced mathematical principles and their engineering applications. - Develop skills in numerical analysis and computational methods. - Learn optimization techniques and algorithms. - Explore statistical modeling and data analysis. - Analyze engineering problems using mathematical tools. - Develop critical thinking, problem-solving, and analytical skills. - Gain practical experience through projects and internships. **Major Outline:** 1. **Introduction to Mathematical Engineering** - Overview of mathematical engineering, its principles, and applications in various fields. 2. **Advanced Calculus and Linear Algebra** - In-depth study of calculus and linear algebra with engineering applications. 3. **Numerical Analysis** - Techniques for numerical approximation and solution of mathematical problems. 4. **Optimization Techniques** - Methods for optimizing engineering systems and processes. 5. **Statistical Modeling and Data Analysis** - Principles of statistical modeling, data analysis, and inference. 6. **Differential Equations and Dynamical Systems** - Study of differential equations and their applications in modeling dynamic systems. 7. **Computational Methods** - Using computational tools and software for solving engineering problems. 8. **Engineering Applications of Mathematics** - Applying mathematical principles to solve real-world engineering problems. 9. **Mathematical Modeling** - Techniques for developing and analyzing mathematical models of engineering systems. 10. **Internship/Practicum** - Real-world experiences in mathematical engineering settings, such as engineering firms, research institutions, or data science companies. 11. **Capstone Project** - Comprehensive project applying mathematical engineering skills, such as developing a mathematical model, conducting a numerical analysis, or solving an optimization problem. **Assessment Methods:** - Advanced calculus and linear algebra projects - Numerical analysis and computational methods projects - Optimization techniques and algorithms projects - Statistical modeling and data analysis reports - Differential equations and dynamical systems projects - Engineering applications of mathematics projects - Mathematical modeling projects - Internship/practicum reports - Capstone projects and presentations **Recommended Textbooks:** - "Introduction to Mathematical Engineering" by various authors - "Advanced Calculus and Linear Algebra" by various authors - "Numerical Analysis" by various authors - "Optimization Techniques" by various authors - "Statistical Modeling and Data Analysis" by various authors - "Differential Equations and Dynamical Systems" by various authors - "Computational Methods" by various authors - "Engineering Applications of Mathematics" by various authors - "Mathematical Modeling" by various authors **Prerequisites:** Strong foundation in mathematics, including calculus and linear algebra, and an interest in engineering applications and problem-solving. **Major Duration:** Typically 4 years for a bachelor's degree or 2 years for a master's degree in Mathematical Engineering. **Certification:** Graduates may pursue certifications such as: - Certified Analytics Professional (CAP) from INFORMS - Professional Certificate in Data Science from various institutions - Certifications in specific mathematical or engineering software tools **Target Audience:** Aspiring mathematical engineers, data scientists, financial analysts, researchers, and professionals seeking careers in engineering firms, data science companies, financial institutions, research institutions, and related fields. This major equips students with the advanced mathematical and analytical skills needed to excel in mathematical engineering, supporting careers in various roles within engineering fields, data science, finance, and research institutions.