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Development Programming Languages Python

Programming Numerical Methods in Python

A Practical Approach to Understand the Numerical Methods
Bestseller
Rating: 4.6 out of 54.6 (506 ratings)
2,552 students
Created by Murad Elarbi
Last updated 1/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Program the numerical methods to create simple and efficient Python codes that output the numerical solutions at the required degree of accuracy.
  • Create and manipulate arrays (vectors and matrices) by using NumPy.
  • Use the plotting functions of matplotlib to present your results graphically.
  • Apply SciPy numerical analysis functions related to the topics of this course.
Curated for the Udemy for Business collection

Course content

7 sections • 57 lectures • 12h 18m total length

  • Preview04:50

  • Preview05:40
  • Simple Iterations Method: Code I (for Loop)
    26:25
  • Simple Iterations Method: Code II (while Loop)
    14:22
  • Convergence vs Divergence
    05:53
  • Newton-Raphson Method
    15:28
  • Bisection Method: Algorithm
    11:45
  • Bisection Method: Code
    17:29
  • False Position (Regula Falsi) Mehtod
    13:55
  • Secant Method
    15:49
  • User-Defined Functions & Run-Time Input
    11:02
  • Root Finding in SciPy & Summary
    10:46

  • Preview15:25
  • Lagrange's Method: Algorithm
    07:31
  • Lagrange's Method: Code
    17:33
  • Newton's Method: Algorithm
    10:58
  • Newton's Method: Code
    16:05
  • Linear Regression: Algorithm
    04:08
  • Linear Regression: Code I (for Loop)
    08:16
  • Linear Regression: Code II (NumPy Arrays)
    08:28
  • Polynomial Fit: Algorithm
    04:43
  • Polynomial Fit: Code
    24:00
  • Interpolation Functions of SciPy
    08:58
  • Curve Fitting Functions of SciPy & Summary
    14:53

  • Introduction and Finite Differences Method
    12:05
  • Finite Differences Method: Code I
    11:30
  • Finite Differences Method: Code II
    11:26
  • Plotting Derivative Curves
    17:40
  • Numerical Differentiation Function in SciPy & Summary
    10:00

  • Introduction & Trapezoidal Rule: Algorithm
    07:38
  • Trapezoidal Rule: Code
    11:57
  • Simpson's 1/3 Rule: Algorithm
    07:21
  • Simpson's 1/3 Rule: Code
    08:17
  • Simpson's 3/8 Rule: Algorithm
    05:27
  • Simpson's 3/8 Rule: Code
    09:41
  • Double Integration: Algorithm
    07:54
  • Double Integration: Code
    16:01
  • Quadrature in SciPy & Summary
    14:20

  • Introduction & Gauss Elimination Method: Algorithm
    26:00
  • Gauss Elimination Method: Code I (Elimination)
    21:09
  • Gauss Elimination Method: Code II (Back-Substitution)
    21:40
  • Gauss Elimination Method: Code III (Modifications)
    16:38
  • Jacobi's Method: Algorithm
    07:14
  • Jacobi's Method: Code
    32:07
  • Gauss-Seidel's Method
    10:48
  • Diagonal Dominance
    05:06
  • Linear System Solution in NumPy and SciPy & Summary
    08:36
  • Gauss-Jordan Method: Procedure
    13:56
  • Gauss-Jordan Method: Algorithm & Code
    12:54

  • Introduction & Euler's Method
    16:22
  • Second Order Runge-Kutta's Method
    07:15
  • Fourth Order Runge-Kutta's Method
    08:37
  • Fourth Order Runge-Kutta's Method: Plot Numerical and Exact Solutions
    15:57
  • Higher-Order ODE's: Algorithm
    08:27
  • Higher-Order ODE's: Code
    22:52
  • Higher-Order ODE's: Plotting Solutions
    20:11
  • ODE Solution in SciPy & Summary
    16:46

Requirements

  • You should have a good background in algebra and calculus, in addition to the basic knowledge about computers
  • A Python IDE and its libraries NumPy, matplotlib and SciPy should be installed on your computer.
  • No previous experience in programming in Python is required.

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:

  • The students who currently study their first course in numerical methods and need to understand how the methods are coded in detail.
  • The students who need to create their own numerical analysis codes or use Python numerical libraries for their course, project or thesis works.

Featured review

Vipin P
Vipin P
3 courses
2 reviews
Rating: 4.5 out of 5a year ago
I am a doctoral researcher in Physics. I am used to coding in MATLAB for my numerics. The problem with MATLAB is its cost. I wanted a to learn a tool that would match my requirements while being free. I knew that python would be a great match and all I wanted was an excellent course to get into it. I got it here.

Instructor

Murad Elarbi
Mechanical Engineer, Lecturer
Murad Elarbi
  • 4.6 Instructor Rating
  • 506 Reviews
  • 2,552 Students
  • 1 Course

I am a Mechanical Engineering Lecturer in the University of Benghazi, Libya since 2005. I taught courses of Strength of Materials, Theory of Machines, Machine Design Projects and Engineering Drawing. My research interest is the computational mechanics where numerical methods and computer programming are the main tools of solution in addition to theories of mechanics. I instructed several computer language training courses of BASIC, Fortran, C++ and MATLAB. Currently, I am in the USA for the Ph.D. degree.

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