[  COVER OF THE WEEK ]

Insights  Source

[ AnalyticsWeek BYTES]

>> House of Cards by analyticsweek

>> Who could be a Startup CEO? Ben Horowitz’s 2 cents  by v1shal

>> 21 Must-Read Books for Fintech and Finance Enthusiasts by prasanna

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[ FEATURED COURSE]

Hadoop Starter Kit

Hadoop learning made easy and fun. Learn HDFS, MapReduce and introduction to Pig and Hive with FREE cluster access…. more

[ FEATURED READ]

Superintelligence: Paths, Dangers, Strategies

The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but … more

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ 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]

 @AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

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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]

 @RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

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[ FACT OF THE WEEK]

In 2015, a staggering 1 trillion photos will be taken and billions of them will be shared online. By 2017, nearly 80% of photos will be taken on smart phones.

Sourced from: Analytics.CLUB #WEB Newsletter

[  COVER OF THE WEEK ]

Accuracy check  Source

[ AnalyticsWeek BYTES]

>> Tips to Help Get the Most Value from Data Analytics and Database Support by thomassujain

>> Data Science Models Build on Each Other by analyticsweek

>> Rishad Tobaccowala (@rishad) on restoring the soul of business Work 2.0 Podcast #FutureofWork #Work2dot0 #Podcast by v1shal

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[ FEATURED COURSE]

Baseball Data Wrangling with Vagrant, R, and Retrosheet

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

[ FEATURED READ]

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

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 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:Is it better to have 100 small hash tables or one big hash table, in memory, in terms of access speed (assuming both fit within RAM)? What do you think about in-database analytics?
A: Hash tables:
– Average case O(1)O(1) lookup time
– Lookup time doesn’t depend on size

Even in terms of memory:
– O(n)O(n) memory
– Space scales linearly with number of elements
– Lots of dictionaries won’t take up significantly less space than a larger one

In-database analytics:
– Integration of data analytics in data warehousing functionality
– Much faster and corporate information is more secure, it doesn’t leave the enterprise data warehouse
Good for real-time analytics: fraud detection, credit scoring, transaction processing, pricing and margin analysis, behavioral ad targeting and recommendation engines

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[ VIDEO OF THE WEEK]

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

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[ QUOTE OF THE WEEK]

I’m sure, the highest capacity of storage device, will not enough to record all our stories; because, everytime with you is very valuable da

[ PODCAST OF THE WEEK]

 #FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

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[ FACT OF THE WEEK]

Retailers who leverage the full power of big data could increase their operating margins by as much as 60%.

Sourced from: Analytics.CLUB #WEB Newsletter

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