Many real-world problems cannot be solved exactly. Engineers, scientists, mathematicians, and data scientists rely on numerical methods to approximate solutions, analyze errors, and build models that power modern technology.
In this course, you will learn the fundamental techniques of numerical analysis through a combination of mathematical theory, worked examples, and hands-on Python programming. Rather than simply memorizing algorithms, you will develop an understanding of why numerical methods work, when they fail, and how to evaluate the accuracy of your results.
You will progress through root-finding methods, interpolation, numerical differentiation, numerical integration, and ordinary differential equations. Along the way, you will implement algorithms from scratch in Python and apply them to practical problems drawn from science, engineering, and data analysis.
By the end of the course, you will have built a collection of numerical algorithms, developed a deeper understanding of computational mathematics, and completed a capstone project that demonstrates your ability to solve real-world problems using numerical methods.
What You’ll Learn
- Implement root-finding algorithms such as the Bisection Method, Newton’s Method, and Secant Method.
- Construct interpolating polynomials and estimate interpolation error.
- Approximate derivatives using finite difference methods.
- Compute definite integrals using numerical integration techniques.
- Solve ordinary differential equations using Euler’s Method and Runge-Kutta methods.
- Build numerical algorithms in Python and evaluate their performance.
- Complete a capstone project that showcases your numerical analysis skills.
Who This Course Is For
- Undergraduate mathematics students
- Engineering students
- Physics students
- Data science and machine learning enthusiasts
- Self-learners interested in computational mathematics
- Anyone who wants to move beyond exact solutions and learn how real-world problems are solved numerically
Prerequisites
Students should be comfortable with:
- College Algebra
- Basic Calculus
- Elementary programming concepts (helpful but not required)
No prior experience with numerical analysis is necessary.
Course Features
- Step-by-step lessons
- Downloadable notes and resources
- Python coding demonstrations
- Assignments and projects
- Capstone project
- Lifetime access to course materials
Whether you are preparing for advanced coursework, building computational skills for your career, or simply curious about how computers solve mathematical problems, this course will provide a practical and accessible introduction to numerical analysis with Python.
Course Instructor
