Nov 16, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Human resource  Source

[ AnalyticsWeek BYTES]

>> Automating Data Modeling for the Internet of Things: Accelerating Transformation and Data Preparation by jelaniharper

>> The End of Transformation: Expediting Data Preparation and Analytics with Edge Computing by jelaniharper

>> The Total Customer Experience: How Oracle Builds their Business Around the Customer by bobehayes

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[ NEWS BYTES]

>>
 Mercy Builds a Healthy Data Framework – CIO Insight Under  Analytics

>>
 One Click Retail Publishes Amazon Holiday Strategy Report for Brand Manufacturers – Broadway World Under  Sales Analytics

>>
 HPE, Hedvig announce hybrid cloud storage partnership – Network World Under  Hybrid Cloud

More NEWS ? Click Here

[ FEATURED COURSE]

Baseball Data Wrangling with Vagrant, R, and Retrosheet

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Analytics with the Chadwick tools, dplyr, and ggplot…. more

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

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Antifragile is a standalone book in Nassim Nicholas Taleb’s landmark Incerto series, an investigation of opacity, luck, uncertainty, probability, human error, risk, and decision-making in a world we don’t understand. The… more

[ TIPS & TRICKS OF THE WEEK]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:What is better: good data or good models? And how do you define ‘good”? Is there a universal good model? Are there any models that are definitely not so good?
A: * Good data is definitely more important than good models
* If quality of the data wasn’t of importance, organizations wouldn’t spend so much time cleaning and preprocessing it!
* Even for scientific purpose: good data (reflected by the design of experiments) is very important

How do you define good?
– good data: data relevant regarding the project/task to be handled
– good model: model relevant regarding the project/task
– good model: a model that generalizes on external data sets

Is there a universal good model?
– No, otherwise there wouldn’t be the overfitting problem!
– Algorithm can be universal but not the model
– Model built on a specific data set in a specific organization could be ineffective in other data set of the same organization
– Models have to be updated on a somewhat regular basis

Are there any models that are definitely not so good?
– ‘all models are wrong but some are useful” George E.P. Box
– It depends on what you want: predictive models or explanatory power
– If both are bad: bad model

Source

[ VIDEO OF THE WEEK]

Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

 Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

#FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

 #FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

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[ FACT OF THE WEEK]

Three-quarters of decision-makers (76 per cent) surveyed anticipate significant impacts in the domain of storage systems as a result of the “Big Data” phenomenon.

Sourced from: Analytics.CLUB #WEB Newsletter

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