Deep data science is a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus these techniques also belong to deep data science. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation.
Some of the features that characterize deep data science, at least the way I do it,, includes:
Deep data science: recommended articles
The picture is from the last article
Putting it together
For a robust regression that will work even if all the traditional model assumptions are violated, click here. It is simple (it can be implemented in Excel and it is model-free), efficient and very comparable to the standard regression (when the model assumptions are not violated). And if you need confidence intervals for the predicted values, you can use the simple model-free confidence intervals (CI) described here. These CIs are equivalent to those being taught in statistical courses, but you don't need to know stats to understand how they work, and to use them. Finally, to measure goodness-of-fit, instead of R-Squared or MSE, you can use this metric, which is more robust against outliers.