Over the years I have formed a view that data science has 4 cornerstones.
Maths & statistics is certainly one of them. A good understanding of the underlying principles is always going to be a strength but in my opinion the academic courses like under-graduate and post graduate degrees tend to over emphasise this one to the detriment of the others, not that it is unimportant of course.
The second cornerstone is programming and associated skills like SQL. Python, R, Jupyter Notebooks etc. are staples of the data science world and being proficient at coding is an important skill.
Next comes visualisation and communication. What use are all those dashboards, reports and models if you cannot transform them into compelling visualisations and then communicate the value and meaning to decision makers?
Finally comes domain expertise and other soft skills. I include psychology in this corner and there is a whole area of understanding and avoiding decision bias which is critically important and often over-looked. Also domain expertise - knowing about the data relevant to your firm and sector and all of its nuances and hidden meanings is very important.
I am yet to meet any individual who has world class skills in all 4 cornerstones. The true answer to the question asked is that data leaders (and I am looking at myself here!) must build teams with cognitive diversity that, when added together, provide coverage in all 4 areas such that the team is highly effective producing extraordinary results.
My advice to aspiring data scientists is not to be daunted by the sheer volume of diverse knowledge of the subject - be excited and enthused by it. You will never get bored of the subject! Pick the area you are best at and become excellent at it and also work at improving your knowledge and expertise in the other areas.
And finally do not be put off by all of those online posts about how nearly-impossible it is to land your dream job in data science. Work hard and love your subject and anything is possible!
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