"Show your work!Yes, you got the answer right.
I am still marking it wrong since I don't know how you did it."
I hated that phrase.
Yes, I heard it more than once.
Teachers never explained to me the point of showing your work is so that if you do in fact come up with the wrong answer they can assist you in how to tweak your algorithm.
I don't think any teacher would have ever explained things this way.
Honestly, most of my teachers I am not convinced they actually understood this is what they were doing.
They really only knew one method of solving a given problem, and in order for them to supply help the students had to follow their methods.
(The majority of my early education was in religion based schools, so these teachers were not Scientists, Mathematicians, etc...)
Now to today, I am a Data Scientist. Which means, I work with numbers, algorithms, Data, business users, technical experts, Architects, Statisticians, and in Domain experts.
I spend much of my day munging data into data structures, or algorithms that provide insight into our data, our users, and our customers. One thing I see Data Scientists doing, and not necessarily talking about is the whole "Show your Work" philosophy.
If you are going to make a claim about an insight you have about data, you should show how you got to that conclusion. In many papers it may be in a section like "Methods and Assumptions", but I think this is important in even internal presentations to business users. You should be able to show others how you got to whatever conclusion you have come to.It may not be "Page 1", (actually Page 1 should really be your final conclusion, but that is another story), but for any presentation in addition to citing your sources, you should touch on any methodologies you followed.
For example, in my current research, I am evaluating Markov chain sequences of behavior patterns. I will write a separate blog about Markov chains at some point, but part of my foundation work has been to show how to both collect the data, then munge my observations into a probability transition matrix.
This has re-iterated to me that we in the data science community have a responsibility to be able to show our work.
Not everyone will want to go into the same weeds, rabbit holes, and other detailed work that you have done. But after taking any number of wrong turns during your analysis, you should be able to show from first principles how you arrived at your final conclusion. Even if that is saved for a smaller presentation for those willing to go through your process with you.
Ad astra!
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