Apr 05, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Tour of Accounting  Source

[ AnalyticsWeek BYTES]

>> Uber: When Big Data Threatens Local Democracy by analyticsweekpick

>> The Business of Data by analyticsweekpick

>> Creating Your Own Threat Intel Through ‘Hunting’ & Visualization by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Scala’s Intelligent Visual and Consumer Engagement Solutions on … – MarTech Series Under  Marketing Analytics

>>
 The best content marketers think like data scientists – TechHQ – TechHQ Under  Social Analytics

>>
 Cloudera brings the Shared Data Experience to its machine learning … – SDTimes.com Under  Streaming Analytics

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[ FEATURED COURSE]

R, ggplot, and Simple Linear Regression

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Begin to use R and ggplot while learning the basics of linear regression… more

[ FEATURED READ]

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th Edition

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The eagerly anticipated Fourth Edition of the title that pioneered the comparison of qualitative, quantitative, and mixed methods research design is here! For all three approaches, Creswell includes a preliminary conside… more

[ TIPS & TRICKS OF THE WEEK]

Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.

[ DATA SCIENCE Q&A]

Q:Do you know / used data reduction techniques other than PCA? What do you think of step-wise regression? What kind of step-wise techniques are you familiar with?
A: data reduction techniques other than PCA?:
Partial least squares: like PCR (principal component regression) but chooses the principal components in a supervised way. Gives higher weights to variables that are most strongly related to the response

step-wise regression?
– the choice of predictive variables are carried out using a systematic procedure
– Usually, it takes the form of a sequence of F-tests, t-tests, adjusted R-squared, AIC, BIC
– at any given step, the model is fit using unconstrained least squares
– can get stuck in local optima
– Better: Lasso

step-wise techniques:
– Forward-selection: begin with no variables, adding them when they improve a chosen model comparison criterion
– Backward-selection: begin with all the variables, removing them when it improves a chosen model comparison criterion

Better than reduced data:
Example 1: If all the components have a high variance: which components to discard with a guarantee that there will be no significant loss of the information?
Example 2 (classification):
– One has 2 classes; the within class variance is very high as compared to between class variance
– PCA might discard the very information that separates the two classes

Better than a sample:
– When number of variables is high relative to the number of observations

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

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[ QUOTE OF THE WEEK]

Data beats emotions. – Sean Rad, founder of Ad.ly

[ PODCAST OF THE WEEK]

@TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

 @TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

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[ FACT OF THE WEEK]

Decoding the human genome originally took 10 years to process; now it can be achieved in one week.

Sourced from: Analytics.CLUB #WEB Newsletter

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