Nov 01, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Accuracy check  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> The User Experience of State Government Websites by analyticsweek

>> Marginal gains: the rise of data analytics in sport by analyticsweekpick

>> The Pitfalls of Using Predictive Models by bobehayes

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[ NEWS BYTES]

>>
 How to Avoid the Trap of Fragmented Security Analytics – Security Intelligence (blog) Under  Analytics

>>
 Are You Spending Too Much (or Too Little) on Cybersecurity? – Data Center Knowledge Under  Data Center

>>
 Most UK businesses are not insured against security breaches and data loss, says study – Information Age Under  Data Security

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[ FEATURED COURSE]

Python for Beginners with Examples

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A practical Python course for beginners with examples and exercises…. more

[ FEATURED READ]

Superintelligence: Paths, Dangers, Strategies

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The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but … more

[ TIPS & TRICKS OF THE WEEK]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:You have data on the durations of calls to a call center. Generate a plan for how you would code and analyze these data. Explain a plausible scenario for what the distribution of these durations might look like. How could you test, even graphically, whether your expectations are borne out?
A: 1. Exploratory data analysis
* Histogram of durations
* histogram of durations per service type, per day of week, per hours of day (durations can be systematically longer from 10am to 1pm for instance), per employee…
2. Distribution: lognormal?

3. Test graphically with QQ plot: sample quantiles of log(durations)log?(durations) Vs normal quantiles

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA

 @AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA

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[ QUOTE OF THE WEEK]

Everybody gets so much information all day long that they lose their common sense. – Gertrude Stein

[ PODCAST OF THE WEEK]

Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

 Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

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[ FACT OF THE WEEK]

Brands and organizations on Facebook receive 34,722 Likes every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

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