Nov 05, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data interpretation  Source

[ AnalyticsWeek BYTES]

>> Enhancing the Security of Cloud-Native Tools and Apps by analyticsweekpick

>> Where Chief Data Scientist & Open Source Meets – @dandegrazia #FutureOfData #Podcast by v1shal

>> Most MAT and school leaders see tech as essential part of ‘new normal’ education by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

CS229 – Machine Learning

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This course provides a broad introduction to machine learning and statistical pattern recognition. … more

[ FEATURED READ]

Storytelling with Data: A Data Visualization Guide for Business Professionals

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Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. Th… more

[ TIPS & TRICKS OF THE WEEK]

Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.

[ 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]

#BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

 #BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The world is one big data problem. – Andrew McAfee

[ PODCAST OF THE WEEK]

#GlobalBusiness at the speed of The #BigAnalytics

 #GlobalBusiness at the speed of The #BigAnalytics

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

39 percent of marketers say that their data is collected ‘too infrequently or not real-time enough.’

Sourced from: Analytics.CLUB #WEB Newsletter

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