Faculty: INSTITUTE OF GRADUATE PGROGRAMS The Computational Science and Engineering (CSE) major integrates principles from computer science, applied mathematics, and engineering to solve complex problems using computational methods. Students gain skills in algorithm development, numerical analysis, high-performance computing, data analytics, and scientific computing, preparing for careers in research, industry, and academia. **Learning Objectives:** - Understand the principles of computational science and engineering. - Develop skills in algorithm development and numerical analysis. - Learn high-performance computing and parallel processing techniques. - Explore data analytics and scientific computing methods. - Analyze computational modeling and simulation techniques. - Develop critical thinking, problem-solving, and analytical skills. - Gain practical experience through projects and internships. **Major Outline:** 1. **Introduction to Computational Science and Engineering** - Overview of CSE principles, history, and current trends. 2. **Algorithm Development** - Fundamentals of designing and analyzing algorithms. 3. **Numerical Analysis** - Techniques for solving mathematical problems using numerical methods. 4. **High-Performance Computing** - Principles of parallel processing and high-performance computing systems. 5. **Data Analytics** - Methods for analyzing and interpreting large datasets. 6. **Scientific Computing** - Use of computational methods to solve scientific and engineering problems. 7. **Computational Modeling and Simulation** - Techniques for developing and implementing computational models. 8. **Parallel Programming** - Principles of writing programs for parallel computing environments. 9. **Optimization Techniques** - Methods for optimizing computational processes and algorithms. 10. **Machine Learning** - Fundamentals of machine learning algorithms and their applications. 11. **Visualization Techniques** - Methods for visualizing data and computational results. 12. **Internship/Practicum** - Real-world experiences in research institutions, industry, or academia. 13. **Capstone Project** - Comprehensive project applying CSE skills, such as developing a computational model, conducting a data analysis project, or implementing a high-performance computing solution. **Assessment Methods:** - CSE principles analyses - Algorithm development projects - Numerical analysis projects - High-performance computing projects - Data analytics projects - Scientific computing projects - Computational modeling and simulation projects - Parallel programming projects - Optimization techniques projects - Machine learning projects - Visualization techniques projects - Internship/practicum reports - Capstone projects and presentations **Recommended Textbooks:** - "Introduction to Computational Science and Engineering" - "Algorithm Development" - "Numerical Analysis" - "High-Performance Computing" - "Data Analytics" - "Scientific Computing" - "Computational Modeling and Simulation" - "Parallel Programming" - "Optimization Techniques" - "Machine Learning" - "Visualization Techniques" **Prerequisites:** Basic knowledge of computer science, mathematics, and an interest in computational science and engineering. **Major Duration:** Typically 4 years for a bachelor's degree in Computational Science and Engineering. **Certification:** Graduates may pursue certifications such as: - Certified Analytics Professional (CAP) - Certified Data Scientist (CDS) - Certifications in specific computational software and technologies **Target Audience:** Aspiring computational scientists, data analysts, scientific computing specialists, high-performance computing engineers, and professionals seeking careers in research institutions, industry, academia, and related fields. This major equips students with the computational, analytical, and problem-solving skills needed to excel in computational science and engineering, supporting careers in various roles within research institutions, industry, academia, and related fields. The program provides a comprehensive education in CSE principles, algorithm development, numerical analysis, high-performance computing, data analytics, scientific computing, computational modeling, parallel programming, optimization techniques, machine learning, visualization techniques, and practical experience through internships and projects, preparing students for successful careers in the field.