This is how I used basic probability to simulate career developmentImage made by author using DALL·EProfessionally speaking, I’m a very weird guy: I work as a Software/Machine Learning Engineer in a startup, I have a Master’s Degree in Physics and I’m about to defend my dissertation for my PhD in Aerospace and Mechanical Engineering. During my ever-changing career, two things stayed the same: my love for science and my passion for coding.A beautiful way to mix science and coding is by doing modeling. What I mean by that is that, in order to describe the world, you make a reasonable assumption based on some degree of approximation of reality. Based on this assumption and on your starting approximation, we can simulate a given process. The simulation will give us some results that stem from the original assumptions but that weren’t exactly predictable before the simulation itself.For example, let’s say that we are trying to figure out how many cows can fit in a fence. A pretty bizarre assumption that a physicist would do is the following:“Let’s consider a squared-shaped cow”Meaning that we approximate the shape of a cow to be the one of a square. Then we approximate the fence to be a bigger square…
Hands on Career Path Modelling Using Markov Chain, with Python | by Piero Paialunga | Jul, 2024
