Computational Physics in Python by Kristjan Haule

About this course

Turn core physics ideas into working code and learn how scientists and engineers explore systems that are too complex for pencil-and-paper math. In this free online course, you will use Python to model, simulate, and analyze physical problems with the mindset of computational physics: choose an approach, implement it efficiently, validate results, and interpret what the numbers mean. It is designed for learners who want more than formulas, aiming to gain practical problem-solving skills that translate to exams, research projects, and technical roles where simulation and data-driven reasoning matter.

Throughout the lessons, you will strengthen your Python foundations in a way that serves computation: working with arrays, slicing and indexing, structured code, and reusable components. You will also see why performance becomes a scientific issue, and how faster numerical workflows can come from optimized libraries, compiled extensions, and parallel thinking. The course builds intuition for accuracy and stability, highlighting why certain numerical choices succeed or fail and how to spot common pitfalls such as accumulated error, sensitivity to initial conditions, or unreliable approximations.

As you progress, you will connect algorithms to real physics use cases. You will work with iterative procedures and root-finding, explore ordinary differential equations as models of dynamical systems, and engage with techniques used in quantum problems such as the Schrödinger equation. You will also develop a practical understanding of randomness as a tool, using Monte Carlo ideas for high-dimensional integration and simulation, along with variance reduction and importance sampling strategies that make results more useful in realistic time.

The course experience emphasizes learning by doing: short exercises and targeted questions help you test understanding, reinforce concepts, and build confidence in translating a physical question into computational steps. By the end, you will be better prepared to read and write scientific Python code, evaluate numerical output critically, and approach new physics problems with a toolbox that scales from classroom assignments to research-style exploration.