Nov 23, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Ethics  Source

[ AnalyticsWeek BYTES]

>> Apr 27, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> How AI is hacking humanity! Lesson from #Brexit & #Election2016 by v1shal

>> Startup Movement Vs Momentum, a Classic Dilemma by v1shal

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[ NEWS BYTES]

>>
 Ditching Engagement in Favor of Blunt-Force Awareness Is a Temptation Marketers Must Avoid – Adweek Under  Social Analytics

>>
 Big Data Set to Get Much Bigger by 2021 – Which-50 (blog) Under  Big Data

>>
 Weak cyber-security protocols can rob companies off clients say experts – Exchange4Media Under  cyber security

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[ FEATURED COURSE]

Hadoop Starter Kit

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Hadoop learning made easy and fun. Learn HDFS, MapReduce and introduction to Pig and Hive with FREE cluster access…. more

[ FEATURED READ]

On Intelligence

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Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:Is it better to design robust or accurate algorithms?
A: A. The ultimate goal is to design systems with good generalization capacity, that is, systems that correctly identify patterns in data instances not seen before
B. The generalization performance of a learning system strongly depends on the complexity of the model assumed
C. If the model is too simple, the system can only capture the actual data regularities in a rough manner. In this case, the system poor generalization properties and is said to suffer from underfitting
D. By contrast, when the model is too complex, the system can identify accidental patterns in the training data that need not be present in the test set. These spurious patterns can be the result of random fluctuations or of measurement errors during the data collection process. In this case, the generalization capacity of the learning system is also poor. The learning system is said to be affected by overfitting
E. Spurious patterns, which are only present by accident in the data, tend to have complex forms. This is the idea behind the principle of Occam’s razor for avoiding overfitting: simpler models are preferred if more complex models do not significantly improve the quality of the description for the observations
Quick response: Occam’s Razor. It depends on the learning task. Choose the right balance
F. Ensemble learning can help balancing bias/variance (several weak learners together = strong learner)
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[ VIDEO OF THE WEEK]

RShiny Tutorial: Turning Big Data into Business Applications

 RShiny Tutorial: Turning Big Data into Business Applications

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[ QUOTE OF THE WEEK]

The data fabric is the next middleware. – Todd Papaioannou

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

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[ FACT OF THE WEEK]

According to Twitter’s own research in early 2012, it sees roughly 175 million tweets every day, and has more than 465 million accounts.

Sourced from: Analytics.CLUB #WEB Newsletter

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