Aug 03, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Productivity  Source

[ AnalyticsWeek BYTES]

>> Periodic Table Personified [image] by v1shal

>> How to Win Business using Marketing Data [infographics] by v1shal

>> October 31, 2016 Health and Biotech analytics news roundup by pstein

Wanna write? Click Here

[ NEWS BYTES]

>>
 Israeli cyber co Waterfall teams with insurance specialists – Globes Under  cyber security

>>
 Call Centers, Listen Up: 3 Steps to Define the Customer Experience at “Hello” – Customer Think Under  Customer Experience

>>
 Australian companies spending up on big data in 2017 – ChannelLife Australia Under  Big Data Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

CS109 Data Science

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Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data managem… more

[ FEATURED READ]

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

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People love statistics. Statistics, however, do not always love them back. The Signal and the Noise, Nate Silver’s brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Da… more

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:Explain the difference between “long” and “wide” format data. Why would you use one or the other?
A: * Long: one column containing the values and another column listing the context of the value Fam_id year fam_inc

* Wide: each different variable in a separate column
Fam_id fam_inc96 fam_inc97 fam_inc98

Long Vs Wide:
– Data manipulations are much easier when data is in the wide format: summarize, filter
– Program requirements

Source

[ VIDEO OF THE WEEK]

Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

 Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

Subscribe to  Youtube

[ 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 #Podcast with @MPFlowersNYC, @enigma_data

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

Subscribe 

iTunes  GooglePlay

[ 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

Jul 27, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data Mining  Source

[ AnalyticsWeek BYTES]

>> March 13, 2017 Health and Biotech analytics news roundup by pstein

>> Jun 01, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> What “Gangnam Style” could teach about branding: 5 Lessons by d3eksha

Wanna write? Click Here

[ NEWS BYTES]

>>
 Apple selloff a boon to short sellers – Times of India Under  Financial Analytics

>>
 Cloud Security Is Not an Either/Or – Security Intelligence (blog) Under  Cloud Security

>>
 Don’t develop for hybrid cloud without hybrid deployment – ComputerWeekly.com (blog) Under  Hybrid Cloud

More NEWS ? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

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Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be ap… more

[ FEATURED READ]

Data Science from Scratch: First Principles with Python

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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn … more

[ TIPS & TRICKS OF THE WEEK]

Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.

[ DATA SCIENCE Q&A]

Q:What are the drawbacks of linear model? Are you familiar with alternatives (Lasso, ridge regression)?
A: * Assumption of linearity of the errors
* Can’t be used for count outcomes, binary outcomes
* Can’t vary model flexibility: overfitting problems
* Alternatives: see question 4 about regularization

Source

[ VIDEO OF THE WEEK]

The History and Use of R

 The History and Use of R

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The goal is to turn data into information, and information into insight. – Carly Fiorina

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data.

Sourced from: Analytics.CLUB #WEB Newsletter

Jul 20, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data security  Source

[ AnalyticsWeek BYTES]

>> Please share your thoughts about Steve Jobs by bobehayes

>> How Big Data Has Changed Finance by anum

>> 6 Customer Experience Practices of Loyalty Leaders by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 ‘Internet of Things’ is vulnerable to hackers – Minneapolis Star Tribune Under  Internet Of Things

>>
 TensorFlow to Hadoop By Way of Datameer – Datanami Under  Hadoop

>>
 HR and IT combine efforts on workforce analytics – CIO Under  Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Hadoop Starter Kit

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

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their … 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:What are confounding variables?
A: * Extraneous variable in a statistical model that correlates directly or inversely with both the dependent and the independent variable
* A spurious relationship is a perceived relationship between an independent variable and a dependent variable that has been estimated incorrectly
* The estimate fails to account for the confounding factor

Source

[ VIDEO OF THE WEEK]

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

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

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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]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Estimates suggest that by better integrating big data, healthcare could save as much as $300 billion a year — that’s equal to reducing costs by $1000 a year for every man, woman, and child.

Sourced from: Analytics.CLUB #WEB Newsletter

Jul 13, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Accuracy check  Source

[ NEWS BYTES]

>>
 Using Emojis to Boost Sentiment Analysis – Datanami Under  Sentiment Analysis

>>
 Software-defined secure networking is ideal for hybrid cloud security – CyberScoop Under  Hybrid Cloud

>>
 Why Big Data Wasn’t Trump’s Achilles Heel After All – Forbes Under  Big Data Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Introduction to Apache Spark

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Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals…. more

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

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A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… more

[ TIPS & TRICKS OF THE WEEK]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:What is latent semantic indexing? What is it used for? What are the specific limitations of the method?
A: * Indexing and retrieval method that uses singular value decomposition to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text
* Based on the principle that words that are used in the same contexts tend to have similar meanings
* “Latent”: semantic associations between words is present not explicitly but only latently
* For example: two synonyms may never occur in the same passage but should nonetheless have highly associated representations

Used for:

* Learning correct word meanings
* Subject matter comprehension
* Information retrieval
* Sentiment analysis (social network analysis)

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data at Work: Paul Sonderegger

 @AnalyticsWeek: Big Data at Work: Paul Sonderegger

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

You can use all the quantitative data you can get, but you still have to distrust it and use your own intelligence and judgment. – Alvin Tof

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Facebook users send on average 31.25 million messages and view 2.77 million videos every minute.

Sourced from: Analytics.CLUB #WEB Newsletter

Jul 06, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Statistics  Source

[ AnalyticsWeek BYTES]

>> Big Data Provides Big Insights for U.S. Hospitals by bobehayes

>> 3 S for Building Big Data Analytics Tool of the Future by v1shal

>> Untangling Big Data for Digital Marketing by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Insurers turn to outsourcing to shore up data security – Information Management Under  Data Security

>>
 Clarabridge Dials Up Customer Connections | CustomerThink – Customer Think Under  Sentiment Analysis

>>
 RISELab Takes Flight at UC Berkeley – Datanami Under  Big Data Security

More NEWS ? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

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This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and n… more

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

image

A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… 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:Why is naive Bayes so bad? How would you improve a spam detection algorithm that uses naive Bayes?
A: Naïve: the features are assumed independent/uncorrelated
Assumption not feasible in many cases
Improvement: decorrelate features (covariance matrix into identity matrix)

Source

[ VIDEO OF THE WEEK]

Agile Data Warehouse Design for Big Data Presentation

 Agile Data Warehouse Design for Big Data Presentation

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Big Data is not the new oil. – Jer Thorp

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

235 Terabytes of data has been collected by the U.S. Library of Congress in April 2011.

Sourced from: Analytics.CLUB #WEB Newsletter

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

[  COVER OF THE WEEK ]

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Insights  Source

[ NEWS BYTES]

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

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

>>
 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

Subscribe to  Youtube

[ 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

Subscribe 

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

Jun 22, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Extrapolating  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Understanding Customer Buying Journey with Big Data by v1shal

>> The Differences Between a Business Analyst & a Data Analyst by anum

>> What Are the 3 Critical Keys to Healthcare Big Data Analytics? by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Traders are loading up on bets against China Evergrande Group … – Business Insider Under  Financial Analytics

>>
 Machine Learning Techniques for Predictive Maintenance – InfoQ.com Under  Machine Learning

>>
 Accepting What You Don’t Know Is Crucial to Detecting Risk – American Banker Under  Risk Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

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This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applicati… more

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their … 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:What is the maximal margin classifier? How this margin can be achieved?
A: * When the data can be perfectly separated using a hyperplane, there actually exists an infinite number of these hyperplanes
* Intuition: a hyperplane can usually be shifted a tiny bit up, or down, or rotated, without coming into contact with any of the observations
* Large margin classifier: choosing the hyperplance that is farthest from the training observations
* This margin can be achieved using support vectors

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

Subscribe to  Youtube

[ 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]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

As recently as 2009 there were only a handful of big data projects and total industry revenues were under $100 million. By the end of 2012 more than 90 percent of the Fortune 500 will likely have at least some big data initiatives under way.

Sourced from: Analytics.CLUB #WEB Newsletter

Jun 15, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data Accuracy  Source

[ AnalyticsWeek BYTES]

>> 5 Steps Required to Building a Best Practice Digital Analytics Function by analyticsweekpick

>> 100 Greatest Quotes On Leadership by v1shal

>> For the airline industry, big data is cleared for take-off by anum

Wanna write? Click Here

[ NEWS BYTES]

>>
 The Rise of Network Functions Virtualization – Virtualization Review Under  Virtualization

>>
 Data Science Up and Down the Ladder of Abstraction – InfoQ.com Under  Data Science

>>
 Wildly inaccurate election forecasts highlight Big Data challenges – ZDNet Under  Big Data Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

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Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be ap… more

[ FEATURED READ]

How to Create a Mind: The Secret of Human Thought Revealed

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Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse… 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:Is it better to spend 5 days developing a 90% accurate solution, or 10 days for 100% accuracy? Depends on the context?
A: * “premature optimization is the root of all evils”
* At the beginning: quick-and-dirty model is better
* Optimization later
Other answer:
– Depends on the context
– Is error acceptable? Fraud detection, quality assurance

Source

[ VIDEO OF THE WEEK]

Surviving Internet of Things

 Surviving Internet of Things

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

In God we trust. All others must bring data. – W. Edwards Deming

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

The largest AT&T database boasts titles including the largest volume of data in one unique database (312 terabytes) and the second largest number of rows in a unique database (1.9 trillion), which comprises AT&T’s extensive calling records.

Sourced from: Analytics.CLUB #WEB Newsletter

Jun 08, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Fake data  Source

[ AnalyticsWeek BYTES]

>> IBM and Hadoop Challenge You to Use Big Data for Good by bobehayes

>> AtScale opens Hadoop’s big-data vaults to nonexpert business users by anum

>> Hacking the Data Science by v1shal

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 Future of the Professions: How Technology Will Transform the Work of Human Experts

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This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … 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:What is a decision tree?
A: 1. Take the entire data set as input
2. Search for a split that maximizes the ‘separation” of the classes. A split is any test that divides the data in two (e.g. if variable2>10)
3. Apply the split to the input data (divide step)
4. Re-apply steps 1 to 2 to the divided data
5. Stop when you meet some stopping criteria
6. (Optional) Clean up the tree when you went too far doing splits (called pruning)

Finding a split: methods vary, from greedy search (e.g. C4.5) to randomly selecting attributes and split points (random forests)

Purity measure: information gain, Gini coefficient, Chi Squared values

Stopping criteria: methods vary from minimum size, particular confidence in prediction, purity criteria threshold

Pruning: reduced error pruning, out of bag error pruning (ensemble methods)

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Big Data Analytics

 @AnalyticsWeek Panel Discussion: Big Data Analytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

In God we trust. All others must bring data. – W. Edwards Deming

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with David Rose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with David Rose, @DittoLabs

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Data production will be 44 times greater in 2020 than it was in 2009.

Sourced from: Analytics.CLUB #WEB Newsletter

Jun 01, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Ethics  Source

[ AnalyticsWeek BYTES]

>> Big Data has Big Implications for Customer Experience Management by bobehayes

>> Optimizing your customer relationship survey by bobehayes

>> Big Data Insights in Healthcare, Part II. A Perspective on Challenges to Adoption by froliol

Wanna write? Click Here

[ NEWS BYTES]

>>
 Red Hat launches OpenShift on Google Cloud – ZDNet Under  Cloud

>>
 Justifying the Hybrid Cloud: It’s All About the Application – IT Business Edge (blog) Under  Hybrid Cloud

>>
 MEF, PNDA Roll Out Initiative Focusing on LSO Analytics – SDxCentral Under  Analytics

More NEWS ? 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]

On Intelligence

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Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… 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: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]

Reimagining the role of data in government

 Reimagining the role of data in government

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data that is loved tends to survive. – Kurt Bollacker, Data Scientist, Freebase/Infochimps

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with  John Young, @Epsilonmktg

 #BigData @AnalyticsWeek #FutureOfData #Podcast with John Young, @Epsilonmktg

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[ FACT OF THE WEEK]

By 2020, we will have over 6.1 billion smartphone users globally (overtaking basic fixed phone subscriptions).

Sourced from: Analytics.CLUB #WEB Newsletter