Python Programming
High School (11–12)
High School (9–10)
Undergraduate
General
Python for Science and Maths › Simulation and RandomnessChapter 3Simulation and Data
Use randomness to simulate experiments, and analyse real tables of data with pandas.
Part of Python Programming · Science and Maths (chapter 3)
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Simulation and Randomness
Chance is a part of science, and the computer can imitate it. In this course you make random numbers with a seeded generator, so that a simulation can be repeated, and see the law of large numbers. You estimate probabilities and the number pi by the Monte Carlo method, and see that its error falls as one over the square root of the number of trials. You simulate random walks and find that the typical distance grows as the square root of the number of steps, which is diffusion. You simulate radioactive decay and see why small samples are noisy and large samples are smooth. A project asks how sure a measurement is: the standard error, a whole experiment repeated thousands of times, the bootstrap, and the difference between random and systematic errors. The practice runs your code and tells you which results are wrong.
Data Analysis with pandas