The Unexpected Pure Math You Have to Know as a Data Scientist : Pythagorean Means

How this theoretical math topic is crucial to data science.
0:00 The Pythagorean Means
5:56 The AM GM Inequality
9:02 The GM HM Inequality
10:55 Link to Data Science
Visuals created with excalidraw.com/

[@jessibenzel243]

This is fantastic. You are so skilled at explaining why mathematical concepts are important in data science. I learned so much from this video!

[@ritvikmath]

Thanks!

[@EdeYOlorDSZs]

clean, quick, fun. You're a talented explainer

[@bin4ry_d3struct0r]

None of these concepts beyond the equations were fleshed out in any of my data science courses. Thanks a bunch!

[@randomcandy1000]

Was just learning this a couple months ago studying for advanced calculus, interesting topic, nice vid.

[@acdundore]

This is a great explanation! Thank you.

[@skippy1234459]

Awesome video! Quick question: since the derivative of the harmonic mean has that squared term, does that mean that the harmonic mean can only ever increase as it 'sees' additional samples? This doesn't make intuitive sense to me since the derivative should be able to be negative to allow for a given data point to 'bring down' the mean (e.g. a terrible recall score will drag down an F1 score even when precision is perfect). I'm out of my depth in trying to check your work on the derivative but let me know if I'm missing something!!

[@dr.kingschultz]

Amazing we need more

[@VictorOrdu]

Thanks 👍🏽

[@oliverfried5818]

awesome, thank you

[@ritvikmath]

You bet!

[@samirelzein1095]

that "let s take a look" at 6:07 sounded strangely like Luis SERRANO :)

[@Set_Get]

thank you

[@ritvikmath]

Welcome!