Re:Foundations of Data Science [pdf]
by itissid on Friday October 07, 2016 @09:54AM
(#12660648)
I was surprised to see so little attention paid to regression. Regression is quite powerful and is a powerful thing in a DS's toolkit.
For example:
- What is the distribution of the residuals, how does it change over time as data comes in. How Gaussian they are(or not), analyzing weird/oddities especially around the tails
- What kind of features offer the most significant signal to the model and which ones are not.
These skills are even applicable to SVM and other classification analysis.
Re:Foundations of Data Science [pdf]
by arahuja on Friday October 07, 2016 @10:49AM
(#12661065)
Agree with this, simple concepts taught in-depth with lots applications and practice is very useful. Knowing 10 ML algorithms superficially will never be as good as knowing 1 with all of it's caveats.
For example: - What is the distribution of the residuals, how does it change over time as data comes in. How Gaussian they are(or not), analyzing weird/oddities especially around the tails - What kind of features offer the most significant signal to the model and which ones are not.
These skills are even applicable to SVM and other classification analysis.