and here' the output vu indeedit does turn out to be zero kurtosis reveals our data distribution in two linked waysthe shape of the tailor the how sharp the peak if just squish the tail down it kind of pushes up that peak to be pointierand likewiseif were to push down that distributionyou can imagine that' kind of spreading things out little bitmaking the tails little bit fatterand the peak of it little bit lower so that' what kurtosis meansand in this examplekurtosis is near zero because it is just plain old normal distribution if you want to play around with itgo ahead andagaintry to modify the distribution make it centered around something besides and see if that actually changes anything should itwellit really shouldn' because these are all measures of the shape of the distributionand it doesn' really say whole lot about where that distribution is exactly it' measure of the shape that' what the moments are all about go ahead and play around with thattry different center valuestry different standard deviation valuesand see what effect it has on these valuesand it doesn' change at all of courseyou' expect things like the mean to change because you're changing the mean valuebut varianceskewmaybe not play aroundfind out there you have percentiles and moments percentiles are pretty simple concept moments sound hardbut it' actually pretty easy to understand how to do itand it' easy in python too now you have that under your belt it' time to move on summary in this we saw the types of data (numericcategoricaland ordinal datathat you might encounter and how to categorize them and how you treat them differently depending on what kind of data you're dealing with we also walked through the statistical concepts of meanmedian and modeand we also saw the importance of choosing between median and meanand that often the median is better choice than the mean because of outliers nextwe analyzed how to compute meanmedianand mode using python in an ipython notebook file we learned the concepts of standard deviation and variance in depth and how to compute them in python we saw that they're measure of the spread of data distribution we also saw way to visualize and measure the actual chance of given range of values occurring in dataset using probability density functions and probability mass functions