Here we discuss general applications of statistical models whether they arise from data science operations research engineering machine learning or statistics we do not discuss specific algorithms such as decision trees logistic regression bayesian modeling markov models data reduction or feature selection. Even classical machine learning and statistical techniques such as clustering density estimation or tests of hypotheses have model free data driven robust versions designed for automated processing as in machine to machine communications and thus also belong to deep data science. Ive put together a short guide for aspiring data scientists particularly focused on statistical models and machine learning models supervised and unsupervised many of these topics are covered in textbooks graduate level statistics courses data science bootcamps and other training resources some of which are included in the reference . This event has reached capacity and registration is now closed you may watch this event live through our streaming service fieldsliveregistration for this event includes attendence to data science in industry at mars with vector institute this is a retrospective workshop for the thematic program statistical models learning and inference for big data held from january to june 2015
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