Every Data Scientist Should Know About Bayesian Networks

Exploring causalities where correlations are not enough

To get to the next level with your analyses, consider Bayesian Networks. Image generated with Leonardo AI

The data science landscape is shifting fast. Basing your analyses on basic techniques like linear regressions, decision trees, and even neural networks is no longer enough.

In business settings, managers and decision makers no longer want to know what the data reveals, but why one datapoint might say something meaningful and another might not. In other words, being able to reveal causal relationships is becoming the norm.

This affects data scientists across many different industries, whether that be tech, finance, healthcare, or another field.

Bayesian networks are one of the key tenets in causal inference. They allow us to effectively describe cause and effect, even in messy, real-world datasets.

Imagine being able to confidently say, “This increase in sales isn’t just correlated with our marketing efforts—it’s a direct result of them.” Or predicting, as is my daily bread and butter, the long-term financial impact of corporate sustainability initiatives, not as a guess, but with grounded, data-driven certainty.

By the time you have finished this article, you’ll have learned what Bayesian networks are and how to use them with libraries like pgmpy. You’ll have seen some real-world applications, too, and will understand how this all can benefit your career.