Feb 21, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Convincing  Source

[ AnalyticsWeek BYTES]

>> Matt Ward(@itsmattward) on #FutureOfJobs and #startups in #eCommence #JobsOfFuture #Podcast by v1shal

>> Why Is Big Data Is So Big In Health Care? by analyticsweek

>> What Crying Baby Could Teach Big Data Discovery Solution Seekers? by v1shal

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[ NEWS BYTES]

>>
 Prescriptive and Predictive Analytics Market by 2025 Overview and Forecast by Consumption, Trend, Vendors, Types … – Business News Under  Talent Analytics

>>
 Who’s afraid of the big, bad hybrid cloud? – ITProPortal Under  Hybrid Cloud

>>
 Adobe Analytics: Black Friday hit $6.2 billion in online sales – WNCT Under  Analytics

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[ FEATURED COURSE]

Intro to Machine Learning

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Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most stra… more

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

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A black swan is an event, positive or negative, that is deemed improbable yet causes massive consequences. In this groundbreaking and prophetic book, Taleb shows in a playful way that Black Swan events explain almost eve… more

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ 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]

Using Analytics to build A #BigData #Workforce

 Using Analytics to build A #BigData #Workforce

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[ QUOTE OF THE WEEK]

You can have data without information, but you cannot have information without data. – Daniel Keys Moran

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with  John Young, @Epsilonmktg

 #BigData @AnalyticsWeek #FutureOfData #Podcast with John Young, @Epsilonmktg

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[ FACT OF THE WEEK]

In 2015, a staggering 1 trillion photos will be taken and billions of them will be shared online. By 2017, nearly 80% of photos will be taken on smart phones.

Sourced from: Analytics.CLUB #WEB Newsletter

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