Information is everywhere. We have information on the menus at our restaurants, in the books in our libraries, and on street signs while we’re driving. But what exactly is information in the context of data science? Information is something that reduces uncertainty about our world. It is the answer to questions like who, what, where, how many, or how much. Essentially, information provides clarity about the world we live in. Information is created from data. We create information by organizing, analyzing, and interpreting data. Organizing, analyzing, and interpreting data gives it context and meaning. This additional context and meaning is essentially what distinguishes raw data from information. For example, imagine that our doctor has a recorded a history of our normal body temperature over the past few years. They analyze the historical data and computes that our average (or normal) body temperature is 37°C. This average temperature of 37°C is what we call information. Information is more meaningful than the individual data points that were used to create it. This makes information quite useful on it’s own. However, information can also be used to create something more powerful it can be used to create knowledge.