Sorry for the bad feedback but I think you really misunderstood what is data science. The checklist you describe here it looks like a "path" to machine learning engineer and not to data science itself.
where is the understanding part of a linear regression model? the formulation of a problem? the exploratory data analysis?
Data science is about solve problems, connect it to the bussiness and aggregate value to the company, this talking in general terms, but the important thing is know why the problem exist and the approachs we can have to solve it.
A professor of my graduation gave a very intersting example about it that he readed in one of his books. Imagine that we are owner of a phone company and our main product it is a serivce that we charge 30 $ by month to use it and we want to decrease the cancellation rate for this product. Ok, the problem is very clear but how do I solve it? Knowledge of classify image, text mining can be useful here but won’t solve the problem.. a time series can help us to, maybe, determine when this theoretical customer will cancel the product but then what? What do we do after that? What are the variables or attributes that most influence the cancellation rate?
And with all this checklist we cannot solve a simple and common day-to-day problem.
Again, sorry for the bad feedback.