[ COVER OF THE WEEK ]
Data security Source
[ LOCAL EVENTS & SESSIONS]
- Sep 16, 2018 #WEB Free Webinar on Big Data with Scala & Spark – Live Instructor Led Session | Limited Seats | Seattle, WA
- Sep 16, 2018 #WEB Free Webinar on Big Data with Scala & Spark – Live Instructor Led Session | Limited Seats | Amsterdam, Netherlands
- Sep 27, 2018 #WEB Free Webinar, External RSVP: Data Modeling the Stock Market & Common Pitfalls
[ AnalyticsWeek BYTES]
>> How to Use MLflow, TensorFlow, and Keras with PyCharm by analyticsweek
>> The Upper Echelons of Cognitive Computing: Deriving Business Value from Speech Recognition by jelaniharper
>> 20 Best Practices for Customer Feedback Programs: Business Process Integration by bobehayes
[ NEWS BYTES]
>>
Senior Analyst, Marketing Analytics – Built In Chicago Under Marketing Analytics
>>
Global Financial Analytics Market 2017-2026 By Raw Materials, Manufacturing Expenses And Process Analysis – DailyHover Under Financial Analytics
>>
5 Tactics That Separate Analytics Leaders From Followers – Forbes Under Analytics
[ FEATURED COURSE]
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[ FEATURED READ]
Superintelligence: Paths, Dangers, Strategies
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[ TIPS & TRICKS OF THE WEEK]
Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.
[ DATA SCIENCE Q&A]
Q:Explain selection bias (with regard to a dataset, not variable selection). Why is it important? How can data management procedures such as missing data handling make it worse?
A: * Selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved
Types:
– Sampling bias: systematic error due to a non-random sample of a population causing some members to be less likely to be included than others
– Time interval: a trial may terminated early at an extreme value (ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all the variables have similar means
– Data: cherry picking, when specific subsets of the data are chosen to support a conclusion (citing examples of plane crashes as evidence of airline flight being unsafe, while the far more common example of flights that complete safely)
– Studies: performing experiments and reporting only the most favorable results
– Can lead to unaccurate or even erroneous conclusions
– Statistical methods can generally not overcome it
Why data handling make it worse?
– Example: individuals who know or suspect that they are HIV positive are less likely to participate in HIV surveys
– Missing data handling will increase this effect as its based on most HIV negative
-Prevalence estimates will be unaccurate
Source
[ VIDEO OF THE WEEK]
#GlobalBusiness at the speed of The #BigAnalytics
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[ QUOTE OF THE WEEK]
I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding. Hal Varian
[ PODCAST OF THE WEEK]
#BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA
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[ FACT OF THE WEEK]
More than 200bn HD movies which would take a person 47m years to watch.