Not to nitpick but two visuals does not a few make, it only makes a couple. Also what is the source of the visual aids? As they are not referenced I assume you created them. A for effort but T for nice try.

I am not sure where to begin. Your “Top ten algorithms used by data scientists” simply does not make sense. All of the things that you list as examples of algorithms are instead (for the most part) types of analysis and/or activities. Certainly algorithms can be designed that use any or all of those types of analysis (clustering, regression, etc.) or execute those activities/output data of type x (text mining, visualization) but they themselves are no more algorithms then addition or multiplication are equations. Decision trees/rules I suppose you might consider algorithms if you squint hard enough. Many algorithms definitely use decision trees as part of their design and an algorithm at its most basic level is really nothing more than a a set of rules much like a mathematical equation. Still one heck of a stretch to call them algorithms and include them in a top ten list.

Then we have a couple of real head scratchers. Statistics is definitely not an algorithm. It is sometimes considered a branch of mathematics but is usually considered an entire field of study in its own right. You can get four year bachelors, masters, and even Ph.D. degrees in statistics. Of course some but not all algorithms use statistics and statistical analysis, but the field of statistics itself can in no way be considered an algorithm, nor is there any other definition of statistics that would come close to that usage.

I could go on for about three more pages and I have not even touched on the myriad problems in the data science Venn diagram. I thought I should stop though and give you a chance to respond. I am thinking maybe I am missing something?

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Research scientist (Ph.D. micro/mol biology), Thought middle manager, Everyday junglist, Selecta (Ret.), Boulderer, Cat lover, Fish hater

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