Dec 19, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Insights  Source

[ AnalyticsWeek BYTES]

>> State of #FutureOfData in 2017 by admin

>> Office Depot Stitches Together the Customer Journey Across Multiple Touchpoints by analyticsweek

>> How to Save and Reuse Data Preparation Objects in Scikit-Learn by administrator

Wanna write? Click Here

[ FEATURED COURSE]

Machine Learning

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6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending … more

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

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A black swan is an event, positive or negative, that is deemed improbable yet causes massive consequences. In this groundbreaking and prophetic book, Taleb shows in a playful way that Black Swan events explain almost eve… more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:How to optimize algorithms? (parallel processing and/or faster algorithms). Provide examples for both?
A: Premature optimization is the root of all evil – Donald Knuth

Parallel processing: for instance in R with a single machine.
– doParallel and foreach package
– doParallel: parallel backend, will select n-cores of the machine
– for each: assign tasks for each core
– using Hadoop on a single node
– using Hadoop on multi-node

Faster algorithm:
– In computer science: Pareto principle; 90% of the execution time is spent executing 10% of the code
– Data structure: affect performance
– Caching: avoid unnecessary work
– Improve source code level
For instance: on early C compilers, WHILE(something) was slower than FOR(;;), because WHILE evaluated “something” and then had a conditional jump which tested if it was true while FOR had unconditional jump.

Source

[ VIDEO OF THE WEEK]

Agile Data Warehouse Design for Big Data Presentation

 Agile Data Warehouse Design for Big Data Presentation

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[ QUOTE OF THE WEEK]

Getting information off the Internet is like taking a drink from a firehose. – Mitchell Kapor

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

 #BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

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[ FACT OF THE WEEK]

Every second we create new data. For example, we perform 40,000 search queries every second (on Google alone), which makes it 3.5 searches per day and 1.2 trillion searches per year.In Aug 2015, over 1 billion people used Facebook FB +0.54% in a single day.

Sourced from: Analytics.CLUB #WEB Newsletter

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