Faculty: INSTITUTE OF GRADUATE PGROGRAMS **Major Title: Mathematics and Computer Science** **Major Description:** This interdisciplinary major combines mathematics and computer science, focusing on their theoretical foundations and practical applications. Students explore algebra, calculus, discrete mathematics, algorithms, data structures, and software engineering. The program emphasizes analytical thinking, problem-solving skills, and the application of mathematical and computational principles. Graduates are prepared for careers in software development, data science, research, and various technological fields. **Learning Objectives:** - Understand the fundamental principles of mathematics and computer science. - Develop skills in mathematical analysis, algorithm design, and software development. - Learn techniques for solving complex problems using mathematical and computational methods. - Explore the role of mathematics and computer science in technological advancements. - Understand data analysis, ethical considerations, and professionalism in the field. - Analyze challenges and opportunities in mathematics and computer science. - Develop teamwork and problem-solving skills for interdisciplinary projects. **Major Outline:** 1. **Introduction to Mathematics and Computer Science** - Overview of key concepts and history. - Fundamentals of mathematical and computational principles. 2. **Algebra and Calculus** - Study of linear algebra, abstract algebra, number theory, and calculus. - Techniques for solving mathematical problems. 3. **Discrete Mathematics** - Principles of combinatorics, graph theory, and logic. - Applications in computer science. 4. **Algorithms and Data Structures** - Fundamentals of algorithm design, analysis, and implementation. - Techniques for using data structures in software development. 5. **Programming and Software Engineering** - Principles of programming languages, software design, and development methodologies. - Techniques for developing and maintaining software applications. 6. **Data Analysis and Machine Learning** - Methods for data analysis, statistical methods, and machine learning algorithms. - Techniques for applying data analysis to real-world problems. 7. **Theoretical Computer Science** - Principles of computability, complexity theory, and formal languages. - Techniques for understanding theoretical concepts in computer science. 8. **Ethical Considerations and Professionalism** - Understanding ethical considerations and professional standards. - Techniques for ensuring ethical conduct and professionalism. 9. **Emerging Trends** - Analysis of innovations such as artificial intelligence and quantum computing. - Techniques for adapting to new trends and technologies. 10. **Capstone Project** - Real-world project to apply learned skills in mathematical analysis, algorithm design, or software development. - Techniques for delivering a comprehensive solution to complex problems. **Assessment Methods:** - Mathematical analysis projects, algorithm designs, programming assignments, data analysis reports, research papers, group projects, and internships. **Recommended Textbooks:** - "Linear Algebra and Its Applications" by David C. Lay. - "Calculus" by James Stewart. - "Introduction to the Theory of Computation" by Michael Sipser. - "Algorithms" by Robert Sedgewick and Kevin Wayne. **Prerequisites:** Strong foundation in mathematics and basic knowledge of computer science principles. Suitable for students interested in mathematics, computer science, data science, and related fields. **Major Duration:** Typically 4 years for a bachelor's degree, including coursework, projects, and internships. **Certification:** Graduates may receive a degree in Mathematics and Computer Science and pursue professional certifications in data science, software engineering, or related fields. **Target Audience:** Aspiring mathematicians, computer scientists, data scientists, software engineers, and researchers seeking to specialize in mathematics and computer science. This major equips students with the skills needed to excel in mathematics and computer science, supporting advancements in technological innovation and problem-solving.