Dec 06, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Correlation-Causation  Source

[ AnalyticsWeek BYTES]

>> The Last Layer of Cyber Security: Business Continuity and Disaster Recovery with Incremental Backups by jelaniharper

>> Boeing creates data analytics group by analyticsweekpick

>> How the Right Loyalty and Operational Metrics Drive Service Excellence – Webinar by bobehayes

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[ NEWS BYTES]

>>
 GSA Releases Customer Experience Playbook – Nextgov Under  Customer Experience

>>
 Global Healthcare Financial Analytics Market expected to reach at an Extensive rate through Growth analysis – RBTE Report Under  Financial Analytics

>>
 TTEC Announces Global Launch of Humanify™ Insights Platform – Directors Club Newswire (press release) (blog) Under  Sales Analytics

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[ FEATURED COURSE]

Introduction to Apache Spark

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Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals…. more

[ FEATURED READ]

The Future of the Professions: How Technology Will Transform the Work of Human Experts

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This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … 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:Which kernels do you know? How to choose a kernel?
A: * Gaussian kernel
* Linear kernel
* Polynomial kernel
* Laplace kernel
* Esoteric kernels: string kernels, chi-square kernels
* If number of features is large (relative to number of observations): SVM with linear kernel ; e.g. text classification with lots of words, small training example
* If number of features is small, number of observations is intermediate: Gaussian kernel
* If number of features is small, number of observations is small: linear kernel

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

 @AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

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[ QUOTE OF THE WEEK]

In God we trust. All others must bring data. – W. Edwards Deming

[ PODCAST OF THE WEEK]

Understanding #FutureOfData in #Health & #Medicine - @thedataguru / @InovaHealth #FutureOfData #Podcast

 Understanding #FutureOfData in #Health & #Medicine – @thedataguru / @InovaHealth #FutureOfData #Podcast

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[ FACT OF THE WEEK]

Poor data can cost businesses 20%–35% of their operating revenue.

Sourced from: Analytics.CLUB #WEB Newsletter

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