From drug discovery to species classification, credit scoring to cybersecurity and more, the random forest is a popular and powerful algorithm for modeling our complex world. Its versatility and predictive prowess would seem to require cutting-edge complexity, but if we dig into what a random forest actually is, we see a shockingly simple set of repeating steps.

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Welcome to the third post in our series on how to enter data science! The first post covered how to navigate the broad diversity of data science roles in the industry, and the second was a deep dive on (some!) statistics essential to being an effective data scientist. In this post, we’ll cover skills you’ll need when manipulating and analyzing data. Get ready for lots of syntax highlighting!

After hearing so much about Andrew Ng’s famed Machine Learning Coursera course, I started taking the course and loved it. (His demeanor can make any topic sound reassuringly simple!) Early in the course, Ng covers linear regression via gradient descent. In other words, given a series of points, how can we find the line that best represents those points? And to take it a step further, how can we do that with machine learning?