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Numerical Methods with Python for Engineering
Rating: 4.3 out of 5(12 ratings)
889 students

Numerical Methods with Python for Engineering

Learn Numerical Methods with Python: Roots, Linear Algebra, Integration, and Differential Equations
Created byDr.A. Gnanaprakasam
Last updated 9/2025
English

What you'll learn

  • Apply key numerical methods to solve engineering and scientific problems, including roots, integration, differentiation, and ODEs.
  • Implement and compare numerical algorithms in Python using NumPy, SciPy, and Matplotlib for accuracy and efficiency.
  • Evaluate errors and approximations in numerical methods, including truncation, round-off, and error propagation.
  • Model and solve practical engineering problems, applying suitable numerical techniques and visualizing results in Python.

Explore related topics

Course content

2 sections • 8 lectures • 1h 57m total length

  • Python for Beginners: Variables, Lists, Indexing & Slicing Explained10:20

    Are you new to Python? ? In this beginner-friendly tutorial, I explain Python variables, lists, list indexing, and slicing step by step with simple examples. Perfect for students, programmers, and anyone starting their Python journey. ✅ What you’ll learn in this video: What are variables in Python? How to create and use a list List indexing explained (positive & negative indexing) List slicing with step-by-step examples ? By the end, you’ll understand how to store data in Python and access it effectively using indexing and slicing!

  • Python For Loop Explained | Using Lists & Range() Function ?8:49

    In this beginner-friendly Python tutorial, I explain the for loop, how it works with lists, and how to use the range() function in simple steps. ? ✅ What you’ll learn in this video: How for loops work in Python Using a for loop with lists Iterating with the range() function range() syntax explained clearly Step-by-step coding examples for beginners ? By the end of this video, you’ll be able to write Python loops confidently and use range() effectively in your programs.

  • NumPy for Numerical Methods in Python | Complete Beginner Tutorial for Engineers31:17
  • Matplotlib plt.plot() vs plt.scatter() Explained | Python Data Visualization14:54

    Learn how to use Matplotlib in Python to create both line plots (plt.plot) and scatter plots (plt.scatter) step by step. ? In this beginner-friendly tutorial, we cover: ✅ The difference between line plots and scatter plots ✅ How to customize colors, markers, and styles ✅ When to use plt.plot() vs plt.scatter() ✅ Hands-on coding examples with NumPy data By the end, you’ll be able to visualize data effectively using Matplotlib — one of the most powerful Python plotting libraries. ? Perfect for students, beginners, and anyone learning Python data visualization.

  • Python If, Elif, Else Explained with Examples | Python Conditional Statement10:47

    Welcome to the first lecture in our Python programming series! ? 

    In this video, we will learn about conditional statements in Python – `if`, `elif`, and `else`. 

    These are the building blocks of decision-making in any program. 


    ✨ What you will learn in this lecture:

    - How the `if` statement works in Python

    - Using `elif` to check multiple conditions

    - Applying `else` when all other conditions fail

    - Real-life examples to understand the logic

    - Writing clean and clear conditional code


    By the end of this video, you’ll be confident in writing your own Python programs that make decisions automatically. 

  • Python List Comprehension & Filtering Explained with Factory Example | Beginner1:44

    Imagine you’re running a factory with a conveyor belt of items. ?
    In this video, we’ll learn how to use Python list comprehension and filtering with a fun, real-world factory analogy.

    ? What you’ll learn:

    • How to filter items in a list (inspector strategy ?️)

    • How to transform items in a list (worker strategy ?)

    • Python syntax for list comprehensions and filtering

    • Easy-to-follow coding examples

    Perfect for beginners who want to understand Python in a simple and practical way!

    ? Topics covered:

    • Python lists

    • List comprehension

    • Filtering data

    • Python coding for beginners

  • Python While Loops Explained: From print('Hello') to Newton-Raphson Method2:32

    Learn Python while loops step by step in this beginner-friendly tutorial! ?
    We’ll start simple with print("Hello"), move to printing numbers from 1 to 10, and then dive into a real-world application: solving equations with the Newton-Raphson method in Python.

    ? What you’ll learn in this video:

    • Basics of Python while loops with clear examples

    • Printing values using loops (Hello and numbers 1–10)

    • Applying loops to solve equations

    • Coding Newton-Raphson method from scratch in Python

    This tutorial is perfect for Python beginners, students, and anyone looking to strengthen their programming fundamentals. By the end, you’ll understand loops not just theoretically—but also how they solve real-world problems.

Requirements

  • Mathematics: Basic knowledge of calculus (derivatives, integrals) and linear algebra (matrices, vectors).
  • Programming: Prior experience with any programming language (Python preferred, but not required).
  • Tools: A computer with Python installed (Anaconda or Miniconda recommended) and access to Jupyter Notebook.
  • Mindset: Curiosity to apply numerical methods to real-world engineering and science problems.

Description

Numerical methods form the backbone of modern engineering and scientific problem-solving, enabling us to tackle problems that cannot be solved analytically. This course, Numerical Methods with Python for Engineering, is designed to give learners a solid foundation in both the theory and practical application of numerical techniques using Python and its scientific libraries.

The course begins with an introduction to the scientific Python ecosystem, including Jupyter, NumPy, SciPy, and Matplotlib. Even if learners have prior programming experience, this module ensures they are comfortable working with Python’s core tools for scientific computing.

Next, students explore the fundamental concepts of approximations and errors, gaining an understanding of accuracy, precision, and error propagation in numerical computations. From there, the course progresses to numerical differentiation and integration, where students learn finite difference methods, trapezoidal and Simpson’s rules, adaptive quadrature, and SciPy’s built-in integration routines.

The course then covers systems of linear equations, introducing both direct methods (Gaussian elimination, LU decomposition) and iterative methods (Jacobi, Gauss-Seidel). Students also learn root-finding techniques such as bisection, Newton-Raphson, and fixed-point iteration, with practical implementation in Python.

Further modules include curve fitting and interpolation using regression and splines, followed by solving ordinary differential equations (ODEs) with Euler and Runge-Kutta methods, as well as SciPy’s advanced solvers for stiff and non-stiff systems.

By the end of the course, learners will be able to formulate, implement, and analyze numerical solutions to engineering problems using Python, bridging the gap between mathematical theory and computational practice.

Who this course is for:

  • Undergraduate and postgraduate engineering students who want to strengthen their understanding of numerical methods with practical Python skills.
  • Researchers and professionals in engineering and science seeking to apply computational tools for solving real-world problems.
  • Beginner programmers with basic math knowledge who want to learn how to use Python for scientific and engineering applications.
  • Learners preparing for advanced studies in computational engineering, data science, or applied mathematics.

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