Programming Numerical Methods in Python udemy course free download

What you'll learn:

Numerical Methods in Python Programming

  • Approximate integrals using Trapezoidal rule, Simpson’s 1/3 rule, and Romberg integration
  • Find roots of equations using bisection, False position, newton Raphson, and secant methods
  • Find analytically the optimum min and max of a function
  • Solve Ordinary Differential Equations using Runge Kutta Methods (i.e. Euler, Heun’s, Midpoint, and Ralston Methods in addition to fourth-order Runge Kutta Method
  • Find numerically the optimum min and max using Golden section Search method, newton Raphson Technique, and finally the gradient descent/ascent method
  • Solve Systems of Equations using Gauss elimination
  • Perform curve fitting using regression analysis including linear and polynomial regression in addition to linearization for fitting more complex functions

Requirements::

Description:

Many of the Numerical Analysis courses focus on the theory and derivations of the numerical methods more than the programming techniques. Students get the codes of the numerical methods in different languages from textbooks and lab notes and use them in working their assignments instead of programming them by themselves.

For this reason, the course of Programming Numerical Methods in Python focuses on how to program the numerical methods step by step to create the most basic lines of code that run on the computer efficiently and output the solution at the required degree of accuracy.

This course is a practical tutorial for the students of Numerical Analysis to cover the part of the programming skills of their course.

In addition to its simplicity and versatility, Python is a great educational computer language as well as a powerful tool in scientific and engineering computations. For the last years, Python and its data and numerical analysis and plotting libraries, such as NumPy, SciPy and matplotlib, have become very popular programming language and tool in industry and academia.

That’s why this course is based on Python as programming language and NumPy and matplotlib for array manipulation and graphical representation, respectively. At the end of each section, a number of SciPy numerical analysis functions are introduced by examples. In this way, the student will be able to program his codes from scratch and in the same time use the advanced library functions in his work.

This course covers the following topics:

  • Roots of High-Degree Equations
  • Interpolation and Curve Fitting
  • Numerical Differentiation
  • Numerical Integration
  • Systems of Linear Equations
  • Ordinary Differential Equations

Who this course is for:

Course Details:

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