Taking the Log of My Own i

Log Math Basics for Non-Math Majors

There’s a lot of math I am having to catch back up with and on as I dive deeper into data science, but one of the more rewarding bits of this journey has been in finally understanding the point of taking the log value of a number. I’ve seen the approach used multiple times in statistics courses and in data science programming, but if I’m honest, I never really understood why it was necessary or how it benefitted the process. Today, thanks to the Cornell Data Science Certificate program, I can understand both why working with log values is necessary **and** some of the benefits doing so can provide.

Take a look at the code here, which demonstrates a simple probability via application of the chain rule using a small data set, then manufactures a data set that has enough features that the non-log values get evaluated as zero. I then use the log method to get around this limitation, and finally show that the values I am generating are equivalent to their non-log counterparts.

I’ll paste screenshots below if you just want to see and not execute the code yourself.

A “probility” is just a probability that never did sit-ups. (So it has no “ab”s…)
That log’s got rhythm! (Had to scroll all the way down for the dad joke in this one, until I caught my typo above, so now you get two of them. ‘Grats!)

Data Science, Cloud Computing, and Big Data nerd with a focus on healthcare and a deep-rooted passion for making complex topics easier to understand.

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