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[ COVER OF THE WEEK ]
Big Data knows everything Source
[ AnalyticsWeek BYTES]
>> Emerging Applications of Deep Learning: âMaking Data Speakâ by jelaniharper
>> October 3, 2016 Health and Biotech Analytics News Roundup by pstein
>> How Airbnb Uses Big Data And Machine Learning To Guide Hosts To The Perfect Price by analyticsweekpick
[ FEATURED COURSE]
R Basics – R Programming Language Introduction
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Learn the essentials of R Programming – R Beginner Level!… more |
[ FEATURED READ]
Superintelligence: Paths, Dangers, Strategies
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[ TIPS & TRICKS OF THE WEEK]
Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.
[ DATA SCIENCE Q&A]
Q:When would you use random forests Vs SVM and why?
A: * In a case of a multi-class classification problem: SVM will require one-against-all method (memory intensive)
* If one needs to know the variable importance (random forests can perform it as well)
* If one needs to get a model fast (SVM is long to tune, need to choose the appropriate kernel and its parameters, for instance sigma and epsilon)
* In a semi-supervised learning context (random forest and dissimilarity measure): SVM can work only in a supervised learning mode
Source
[ VIDEO OF THE WEEK]
#BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA
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[ QUOTE OF THE WEEK]
We chose it because we deal with huge amounts of data. Besides, it sounds really cool. Larry Page
[ PODCAST OF THE WEEK]
@AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA
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[ FACT OF THE WEEK]
140,000 to 190,000. Too few people with deep analytical skills to fill the demand of Big Data jobs in the U.S. by 2018.