Exploritory data analysis:The process of examining the descriptive statistics for all features as well as their relationship with the target.
Feature engineering. The process of cleaning data, combining features, splitting features into multiple features, handling missing values, and dealing with text, to mention a few of potentially hundreds of steps.
Algorithm selection and hyperparameter tuning. Keeping up with the “dizzying number” of available algorithms and their quadrillions of parameter combinations and figuring out which work best for the data at hand.
Model diagnostics. Evaluation of top models, including the confusion matrix and different probability cutoffs.