Imagine a baker connecting a data science application to his database and asking it, “How many croissants are we going to sell next Sunday?” The application would simply answer, “According to your recorded data and other factors such as the predicted weather, there is a 90% chance that between 62 and 67 croissants will be sold.”
The baker could then plan accordingly. This is an example of an automated data scientist, a system to which you could throw arbitrary data and get insights or predictions in return.
One key component in making this a reality is the ability to learn a predictive model without specifications from humans besides the data. In the Wolfram Language, this is the role of the functions Classify and Predict. For example, let’s train a classifier to recognize morels from hedgehog mushrooms. Read more here.