Issue #30    Web Version
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[  COVER OF THE WEEK ]

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Ethics  Source

[ AnalyticsWeek BYTES]

>> Emergency Preparation Checklist for Severe Weather by v1shal

>> November 21, 2016 Health and Biotech analytics news roundup by pstein

>> Business Development Strategy Reassessment by martin


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[ NEWS BYTES]

>>  SafeHost to roll out first phase of ‘Switzerland’s largest’ data center – DatacenterDynamics Under  Data Center

>>  National Statistics Office Holds Meetings to Raise Awareness about Afro Peruvian Population – Peru this Week Under  Statistics

>>  Velocify ReportHub Delivers a ‘Fitness Tracker’ for Sales Organizations to Achieve Peak Performance – Broadway World Under  Sales Analytics


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[ FEATURED COURSE]

CS109 Data Science

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Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data managem... more


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The Industries of the Future

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The New York Times bestseller, from leading innovation expert Alec Ross, a “fascinating vision” (Forbes) of what's next for the world and how to navigate the changes the future will bring.... more


[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.


[ 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]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA


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[ QUOTE OF THE WEEK]

Torture the data, and it will confess to anything. – Ronald Coase


[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @MichOConnell, @Tibco

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @MichOConnell, @Tibco


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[ FACT OF THE WEEK]

In that same survey, by a small but noticeable margin, executives at small companies (fewer than 1,000 employees) are nearly 10 percent more likely to view data as a strategic differentiator than their counterparts at large enterprises.


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*This Newsletter is hand-curated and autogenerated using #TEAMTAO & TAO, excuse some initial blemishes. As with any AI, it may get worse before it will get relevant, excuse us with your patience & feedback.
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