Foundations of Computational Science and Engineering using Python

Engineers and scientists increasingly rely on computation to model physical systems, analyze experimental data, and solve complex mathematical equations. This course introduces you to computational science using Python, the leading language for scientific computing. You will transition from manual calculations to writing robust, readable Python scripts that automate numerical simulations and analyze scientific datasets. By reading through structured explanations and practicing with written code exercises, you will build a solid foundation in computational techniques. What you'll learn: - Understand core computational concepts, including floating-point arithmetic, error propagation, and basic scientific algorithms. - Apply modern Python features like type hints and clean code formatting to write maintainable scientific scripts. - Solve linear and non-linear mathematical equations numerically using standard algorithms. - Model physical systems by numerically solving ordinary differential equations. - Analyze and manipulate scientific datasets using foundational libraries like NumPy and modern data structures. - Implement basic testing workflows using pytest to ensure the accuracy of your scientific calculations. The course begins with key mathematical and computational terminology before moving into practical numerical methods and scientific libraries. You will progress from foundational theory to writing structured algorithms for simulating real-world engineering systems. This text-only course is designed for beginner engineers, scientists, and students who have a basic understanding of mathematics and want to learn how to apply Python to computational problems, with no prior programming experience required. Start your journey into computational engineering and master the fundamentals of scientific programming today.