Jun 29, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Insights  Source

[ NEWS BYTES]

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 Defense contractor stored intelligence data in Amazon cloud unprotected – Ars Technica Under  Cloud

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 Qatar firms can meet hybrid cloud challenges – Peninsula On-line Under  Hybrid Cloud

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 The tricky, personal politics of cloud security | Network World – Network World Under  Cloud Security

More NEWS ? Click Here

[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

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Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

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In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Mast… more

[ TIPS & TRICKS OF THE WEEK]

Data Have Meaning
We live in a Big Data world in which everything is quantified. While the emphasis of Big Data has been focused on distinguishing the three characteristics of data (the infamous three Vs), we need to be cognizant of the fact that data have meaning. That is, the numbers in your data represent something of interest, an outcome that is important to your business. The meaning of those numbers is about the veracity of your data.

[ DATA SCIENCE Q&A]

Q:How frequently an algorithm must be updated?
A: You want to update an algorithm when:
– You want the model to evolve as data streams through infrastructure
– The underlying data source is changing
– Example: a retail store model that remains accurate as the business grows
– Dealing with non-stationarity

Some options:
– Incremental algorithms: the model is updated every time it sees a new training example
Note: simple, you always have an up-to-date model but you can’t incorporate data to different degrees.
Sometimes mandatory: when data must be discarded once seen (privacy)
– Periodic re-training in “batch” mode: simply buffer the relevant data and update the model every-so-often
Note: more decisions and more complex implementations

How frequently?
– Is the sacrifice worth it?
– Data horizon: how quickly do you need the most recent training example to be part of your model?
– Data obsolescence: how long does it take before data is irrelevant to the model? Are some older instances
more relevant than the newer ones?
Economics: generally, newer instances are more relevant than older ones. However, data from the same month, quarter or year of the last year can be more relevant than the same periods of the current year. In a recession period: data from previous recessions can be more relevant than newer data from different economic cycles.

Source

[ VIDEO OF THE WEEK]

Decision-Making: The Last Mile of Analytics and Visualization

 Decision-Making: The Last Mile of Analytics and Visualization

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[ QUOTE OF THE WEEK]

He uses statistics as a drunken man uses lamp posts—for support rather than for illumination. – Andrew Lang

[ PODCAST OF THE WEEK]

#FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

 #FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

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iTunes  GooglePlay

[ FACT OF THE WEEK]

More than 200bn HD movies – which would take a person 47m years to watch.

Sourced from: Analytics.CLUB #WEB Newsletter

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