Python projects for educational purposes. The difficulty rating considers both the prerequisite knowledge needed, as well as the level of computational thinking required. The goal is for the student to learn more about python applications, as well as some interesting science
Arranged in order of increasing difficulty.
Difficulty: 1/5
Monte-Carlo visualisation of one facet of number theory
Difficulty: 1/5
Large-dataset processing of a text volume to visualise Zipf's law in linguistics
Difficulty: 2/5
Numerical model of rates of reactions, to predict the yields of a particular product
Difficulty: 2/5
Large-dataset processing of a text volume to determine the information content of the English language
Difficulty: 2/5
Simulation of interbreeding population, and computationally determine the gene pool changes over time
Difficulty: 3/5
Numerical simulation of planetary orbital motion between a massive body and a planet
Difficulty: 3/5
Monte-Carlo collisions between particles to approximate the Boltzmann distribution
Difficulty: 3/5
Numerical simulation of a spring-mass harmonic oscillator with damping terms
Difficulty: 4/5
Numerical model of a Proportional-Integral-Derivative controller, as a learning example of its usage and functionality
Difficulty: 4/5
An unusual derivative of the Predator-Prey model, as an example of modelling the economics of a population
Difficulty: 4/5
Numerical simulation of thermal flow across a pancake