Most of the time, researchers can use the data given to them directly, but there are many instances where the data is not clean. That means it cannot be used directly to train the neural network because it contains data that is not representative of what the algorithm wants to classify. Perhaps it contains bad data, like black-and-white images when you want to create a neural network to locate cats in colored images. Another problem is when the data is not appropriate. For example, when you want to classify images of people as male or female. There might be pictures without the needed tag or pictures that have the information corrupted with misspelled words like “ale” instead of “male.” Even though these might seem like crazy scenarios, they happen all the time. Handling these problems and cleaning up the data is known as data wrangling.