College: Engineering

The Engineering Mathematics major integrates advanced mathematical principles with engineering applications to solve complex real-world problems. Students acquire 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.

Main Curriculum:

1. Introduction to Engineering Mathematics - Overview of engineering mathematics, 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 of numerical approximation and solving mathematical problems.
4. Optimization Techniques - Methods to optimize systems and engineering processes.
5. Statistical Modeling and Data Analysis - Principles of statistical modeling, data analysis, and inference.
6. Differential Equations and Dynamic Systems - Study of differential equations and their applications in modeling dynamic systems.
7. Computational Methods - Utilize computational tools and software to solve engineering problems.
8. Mathematical Applications in Engineering - Apply mathematical principles to solve real-world engineering problems.
9. Mathematical Modeling - Techniques for developing and analyzing mathematical models of engineering systems.
10. Practical/Application Training - Real-world experiences in engineering mathematics settings, such as engineering firms, research institutions, or data science companies.
11. Capstone Project - A comprehensive project applying engineering mathematics skills, such as developing a mathematical model, conducting 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 Dynamic Systems Projects
• Mathematical Applications in Engineering Projects
• Mathematical Modeling Projects
• Practical Training Reports
• Capstone Projects and Presentations

Recommended Textbooks:

• "Introduction to Engineering Mathematics" 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 Dynamic Systems" by various authors
• "Computational Methods" by various authors
• "Mathematical Applications in Engineering" by various authors
• "Mathematical Modeling" by various authors

Prerequisites:

A strong foundation in mathematics, including calculus and linear algebra, and an interest in engineering applications and problem-solving.

Duration of the Major:

Typically 4 years for a Bachelor’s degree or 2 years for a Master’s degree in Engineering Mathematics.

Certification:

Graduates can obtain certifications such as:

• Certified Analytics Professional (CAP) from INFORMS
• Professional Certificate in Data Science from various institutions
• Certificates in specific mathematical or engineering software tools

Target Audience:

Aspiring mathematical engineers, data scientists, financial analysts, researchers, and professionals who seek to work in engineering firms, data science companies, financial institutions, and research organizations or related fields. This major equips students with advanced mathematical and analytical skills essential for excelling in engineering mathematics and supporting careers across diverse roles within engineering, data science, finance, and research institutions.