A Data Science Practitioner's Guide (Part 2: Modelling)

For some reason, data exploration and cleaning are often seen as the lesser-arts of the data science world. This could not be more wrong. EDA is the only way for data scientists to really get a grasp on the problem. Exploring the data is crucial for understanding what the data really represents; rather than what we might think it represents. Indeed data often includes biases (e.g. are the label's representative of the class they are supposed to define?