Data Science

Tesla was one of the earliest thinkers to intuitively grasp the power of information. He meticulously recorded data from his experiments, often analyzing patterns, anomalies, and correlations long before modern computational tools existed. Tesla’s ability to visualize complex systems, run simulations in his mind, and rely on quantitative observation mirrors many of the core principles used in data science today. His emphasis on experimentation, pattern recognition, and predictive modeling aligns with the analytical thinking that drives data-driven decision-making in the modern world.

Though Tesla did not work in what we now call “data science,” his methods anticipated its evolution. He understood that knowledge could be extracted from large sets of observations—what we now refer to as data—and that these insights could be used to improve technology, forecast outcomes, and even reshape entire industries. Today, data science relies on algorithms, machine learning, and advanced statistical models to unlock insights from vast datasets—continuing Tesla’s legacy of using information not just to understand the world, but to transform it.