Shannon entropy is a measure of uncertainty or randomness in a set of data. Developed by mathematician Claude Shannon, it quantifies the amount of information produced by a random variable. Higher entropy indicates more unpredictability, while lower entropy suggests more predictability. In practical terms, Shannon entropy helps in fields like information theory, cryptography, and data compression. For example, a fair coin toss has higher entropy than a biased coin because the outcome is less predictable. By understanding entropy, we can better analyze and manage information in various applications.