📘 Course Title: Computational Mathematics

Code: 29022-AUX

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🏁 Introduction:

Computational Mathematics focuses on solving complex mathematical problems using algorithms, numerical methods, and advanced computer technology. It forms the foundation for simulations, engineering analyses, and scientific discoveries.

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✨ Course Description:

This course provides a comprehensive study of mathematical programming, advanced numerical analysis, scientific computing, and the use of specialized software tools like MATLAB and Python. Students will learn how to develop efficient algorithms and utilize modern computational resources to tackle real-world mathematical challenges.

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🎯 Target Audience:

• Mathematics, Computer Science, and Engineering students.

• Researchers interested in numerical simulations and scientific computation.

• Professionals working in high-performance computing, data science, and modeling.

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📚 What You Will Learn:

• Develop and implement mathematical algorithms and data structures.

• Solve partial differential equations using finite element methods.

• Utilize parallel programming and high-performance computing techniques.

• Apply MATLAB and Python in computational mathematics tasks.

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🧑‍🏫 Instruction Methodology:

• 📖 Interactive Lectures with Theoretical Foundations.

• 💻 Hands-on Coding Sessions and Workshops.

• 🔬 Scientific Project-Based Learning.

• 📈 Continuous Assessment through Assignments and Case Studies.

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🧩 Main Modules:

1️⃣ Mathematical Programming

• 🔢 Mathematical Algorithms

• 🗂️ Data Structures for Efficient Computation

2️⃣ Advanced Numerical Analysis

• 📈 Solving Partial Differential Equations (PDEs)

• 📐 Finite Element Methods (FEM)

3️⃣ Scientific Computing

• 🧮 Parallel Programming Techniques

• 🚀 High-Performance Computing (HPC) Concepts

4️⃣ Mathematical Software Applications

• 📊 Using MATLAB for Numerical Computing

• 🐍 Using Python for Scientific Computation

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🎒 Materials Included:

• 📘 Comprehensive Lecture Notes and Coding Examples

• 🛠️ Access to MATLAB and Python Programming Exercises

• 🖥️ Sample Projects on Scientific Computing

• 🎥 Video Tutorials and Software Demonstrations

• 📑 Weekly Practice Problems and Solutions

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🕒 Course Duration:

• 10 weeks — 2 sessions per week (Each session: 2 hours).

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📈 Level:

• Advanced (Requires prior knowledge in calculus, linear algebra, and basic programming skills).