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DESCRIPTION:Click for Latest Location Information: http://edw2015.dataversity.net/sessionPop.cfm?confid=87&proposalid=7043\nRespondents in a 2012 study conducted by Infochimps and SSWUG.org listed “finding talent” and “finding the right tools” as the most significant challenges faced when working with Big Data. An article in Information Week highlighted that two reasons Big Data projects fail are selecting the wrong uses and asking the wrong questions. Yet, most companies are buying tools and hiring consultants without having knowledge of what is involved in data science. A big part of managing Big Data is asking the right questions. How would an organization know what to ask or whether the data scientist it hires is solving its specific problems using data science techniques? This presentation will provide Enterprise Data World attendees with clear techniques and algorithms that will help in avoiding those failures. \nThis presentation will inform, educate, and above all provide attendees with the confidence they need to hire the right data scientist, know whether the right problems are being addressed, and most importantly prevent failures that can emerge from asking the “wrong questions.” While many techniques and algorithms can be used, this presentation will focus on the main techniques and algorithms needed to do data science. The presenter will cover the following topics in detail:  \nCluster analysis – Big data is complex and using cluster analysis will make it a little easier by grouping data sets according to their similarities. \nNaïve Bayes – Probabilistic classifier that can be used in sentiment analysis and others. \nRegression- Various regression techniques used in predictive analysis \nCorrelation – Differences between correlation and causation\nThe presentation will address the differences between the various algorithms, when algorithms should be used, what kind of questions the algorithms can answer, the most appropriate use cases for the algorithms, and what algorithms works best for specific cases and why.
DTSTART:20150331T111500
SUMMARY:Techniques and Algorithms in Data Science for Big Data
DTEND:20150331T115959
LOCATION: See Description
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