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[  COVER OF THE WEEK ]

Statistically Significant  Source

[ FEATURED COURSE]

R Basics – R Programming Language Introduction

Learn the essentials of R Programming – R Beginner Level!… more

[ FEATURED READ]

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored f… more

[ TIPS & TRICKS OF THE WEEK]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q:Do you know / used data reduction techniques other than PCA? What do you think of step-wise regression? What kind of step-wise techniques are you familiar with?
A: data reduction techniques other than PCA?:
Partial least squares: like PCR (principal component regression) but chooses the principal components in a supervised way. Gives higher weights to variables that are most strongly related to the response

step-wise regression?
– the choice of predictive variables are carried out using a systematic procedure
– Usually, it takes the form of a sequence of F-tests, t-tests, adjusted R-squared, AIC, BIC
– at any given step, the model is fit using unconstrained least squares
– can get stuck in local optima
– Better: Lasso

step-wise techniques:
– Forward-selection: begin with no variables, adding them when they improve a chosen model comparison criterion
– Backward-selection: begin with all the variables, removing them when it improves a chosen model comparison criterion

Better than reduced data:
Example 1: If all the components have a high variance: which components to discard with a guarantee that there will be no significant loss of the information?
Example 2 (classification):
– One has 2 classes; the within class variance is very high as compared to between class variance
– PCA might discard the very information that separates the two classes

Better than a sample:
– When number of variables is high relative to the number of observations

Source

[ VIDEO OF THE WEEK]

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

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[ QUOTE OF THE WEEK]

If we have data, let’s look at data. If all we have are opinions, let’s go with mine. – Jim Barksdale

[ PODCAST OF THE WEEK]

 Unconference Panel Discussion: #Workforce #Analytics Leadership Panel

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[ FACT OF THE WEEK]

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

Sourced from: Analytics.CLUB #WEB Newsletter

[  COVER OF THE WEEK ]

Human resource  Source

[ AnalyticsWeek BYTES]

>> Is It Time to Jump-Start Your Data Offense? by analyticsweek

>> Warehouse Workers Are on the Front Lines of the Covid Crisis. They’re Worried They’ll Be Passed Over for the Vaccine. by awnewsfeed

>> Assessment of Risk Maps in Data Scientist Jobs by thomassujain

Wanna write? Click Here

[ FEATURED COURSE]

The Analytics Edge

This is an Archived Course EdX keeps courses open for enrollment after they end to allow learners to explore content and continue learning. All features and materials may not be available, and course content will not be… more

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

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…. 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:Examples of NoSQL architecture?
A: * Key-value: in a key-value NoSQL database, all of the data within consists of an indexed key and a value. Cassandra, DynamoDB
* Column-based: designed for storing data tables as sections of columns of data rather than as rows of data. HBase, SAP HANA
* Document Database: map a key to some document that contains structured information. The key is used to retrieve the document. MongoDB, CouchDB
* Graph Database: designed for data whose relations are well-represented as a graph and has elements which are interconnected, with an undetermined number of relations between them. Polyglot Neo4J

Source

[ VIDEO OF THE WEEK]

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA

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[ QUOTE OF THE WEEK]

War is 90% information. – Napoleon Bonaparte

[ PODCAST OF THE WEEK]

 Future of HR is more Relationship than Data – Scott Kramer @ValpoU #JobsOfFuture #Podcast

Subscribe 

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[ FACT OF THE WEEK]

According to Twitter’s own research in early 2012, it sees roughly 175 million tweets every day, and has more than 465 million accounts.

Sourced from: Analytics.CLUB #WEB Newsletter

[  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

Wanna write? Click Here

[ 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

Wanna write? Click Here

[ 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

Source

[ 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

Subscribe 

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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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