#### Numerical Analysis & Methods with Python: Theory & Practice

1 Dec 2023

Numerical Analysis & Methods with Python: Theory & Practice

Last updated 10/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 14h 12m | Size: 4.1 GB

Learn Numerical Methods: Linear-algebra, Eigenvalues, Differential Equations, Interpolation, Numerical Analysis & more

What you'll learn
Foundations of Numerical Methods: Understand the fundamental concepts, principles, and techniques used in numerical analysis.
Mathematical Background: Review essential mathematical foundations required for numerical computations, including calculus and linear algebra.
Root-Finding Methods: Learn various algorithms for finding roots of equations, such as the Bisection method, Newton-Raphson method, and Secant method.
Interpolation and Extrapolation: Lagrange interpolation and Newton's divided differences.
Ordinary Differential Equations (ODEs): Solve initial value problems of ODEs using numerical techniques like Euler's method, Runge-Kutta methods (e.g., RK4).
Linear Systems: Learn to solve systems of linear equations using direct methods like Gaussian Elimination, LU decomposition and QR Decomposition.
Linear Systems: Learn to solve systems of linear equations using iterative methods like Jacobi and Gauss-Seidel.
Error Analysis: Understand the sources of error in numerical computations and how to analyze and minimize them
Python Programming: Gain practical experience with Python programming for implementing and solving numerical methods.

Requirements

You should have a basic background in algebra and calculus (derivative, integration,..), in addition some basic programming experiences
Programming Basics: While the course will cover Python programming, some prior programming experience or familiarity with programming concepts would be helpful
It's essential for students to have access to a text editor or an Integrated Development Environment (IDE) to write and execute Python code

Description
Explore the fascinating world of numerical methods and unlock the power of Python programming language for solving complex mathematical and physical problems. In this comprehensive course, you will delve into the essential theoretical foundations of numerical analysis while gaining hands-on experience with practical implementations using Python.

From root-finding, interpolation and numerical integration to solving differential equations and optimization, this course equips you with the necessary mathematical knowledge and programming skills to tackle a wide range of real-world challenges. You'll learn to apply numerical algorithms, understand their strengths and limitations, and analyze their accuracy through rigorous error analysis.

Designed for both aspiring mathematicians and Python enthusiasts, this course strikes a perfect balance between theory and application. Through engaging lectures, interactive coding exercises, and real-world projects, you'll build a strong understanding of numerical methods' underlying principles and learn to implement them effectively with Python libraries like NumPy and SciPy.

Whether you aim to optimize engineering designs, simulate physical phenomena, analyze financial data, or delve into Data Science and Machine Learning, "Numerical Methods with Python" empowers you to confidently approach diverse problems with numerical precision. Join us on this exciting journey, and elevate your problem-solving capabilities to new heights with the synergy of mathematics and Python programming

Who this course is for
Students and Academics: Mathematics, engineering, science, and computer science students or professionals seeking to strengthen their understanding of numerical methods and apply them to real-world scenarios.
Python Enthusiasts: Programmers, data scientists, and analysts interested in expanding their Python skills by exploring numerical analysis and its practical applications.
Anyone Interested in Mathematics and Coding

HOMEPAGE

``  https://www.udemy.com/course/numerical-methods-with-python/ ``

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