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Practical_Statistics_for_Data_Scientist
Statisticalmethodsareakeypartofofdatascience,yetveryfewdatascientistshaveanyformalstatisticstraining.Coursesandbooksonbasicstatisticsrarelycoverthetopicfromadatascienceperspective.Thispracticalguideexplainshowtoapplyvariousstatisticalmethodstodatascience,tellsyouhowtoavoidtheirmisuse,andgivesyouadviceonwhat'simportantandwhat'snot.Manydatascienceresourcesincorporatestatisticalmethodsbutlackadeeperstatisticalperspective.Ifyou'refamiliarwiththeRprogramminglanguage,andhavesomeexposuretostatistics,thisquickreferencebridgesthegapinanaccessible,readableformat.Whyexploratorydataanalysisisakeypreliminarystepindatascience;Howrandomsamplingcanreducebiasandyieldahigherqualitydataset,evenwithbigdata;Howtheprinciplesofexperimentaldesignyielddefinitiveanswerstoquestions;Howtouseregressiontoestimateoutcomesanddetectanomalies;Keyclassificationtechniquesforpredictingwhichcategoriesarecordbelongsto;Statisticalmachinelearningmethodsthat"learn"fromdata;Unsupervisedlearningmethodsforextractingmeaningfromunlabeleddata.
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