Uncategorized – Analytics51

Oct 17, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

SQL Database Source

[ 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 efficiently scrape web data, or collect tons of tweets?
A: * Python example
* Requesting and fetching the webpage into the code: httplib2 module
* Parsing the content and getting the necessary info: BeautifulSoup from bs4 package
* Twitter API: the Python wrapper for performing API requests. It handles all the OAuth and API queries in a single Python interface
* MongoDB as the database
* PyMongo: the Python wrapper for interacting with the MongoDB database
* Cronjobs: a time based scheduler in order to run scripts at specific intervals; allows to bypass the rate limit exceed error

Source

[ VIDEO OF THE WEEK]

George (@RedPointCTO / @RedPointGlobal) on becoming an unbiased #Technologist in #DataDriven World #FutureOfData #Podcast

Subscribe to Youtube

[ QUOTE OF THE WEEK]

We chose it because we deal with huge amounts of data. Besides, it sounds really cool. Larry Page

[ PODCAST OF THE WEEK]

Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

iTunes GooglePlay

[ FACT OF THE WEEK]

For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 10, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Pacman Source

[ AnalyticsWeek BYTES]

>> Impacting Insurance Companyâs bottom line through Big-data by d3eksha

>> Marketing Analytics – Success Through Analysis by analyticsweekpick

>> 15 Metrics for UX Benchmarking by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Data Mining

Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations… more

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

Antifragile is a standalone book in Nassim Nicholas Talebs landmark Incerto series, an investigation of opacity, luck, uncertainty, probability, human error, risk, and decision-making in a world we dont understand. The… 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: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]

#FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

Subscribe to Youtube

[ QUOTE OF THE WEEK]

Data are becoming the new raw material of business. Craig Mundie

[ PODCAST OF THE WEEK]

@CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

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

Oct 03, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Data interpretation Source

[ AnalyticsWeek BYTES]

>> Nov 22, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Tales from the Trenches: Architecting Big Data to Negate Security Threats by jelaniharper

>> What Lessons Presidential Debate Could Teach On Sales by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

CPSC 540 Machine Learning

Machine learning (ML) is one of the fastest growing areas of science. It is largely responsible for the rise of giant data companies such as Google, and it has been central to the development of lucrative products, such … more

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

[ 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: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]

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

Subscribe to Youtube

[ QUOTE OF THE WEEK]

Data is the new science. Big Data holds the answers. Pat Gelsinger

[ PODCAST OF THE WEEK]

Nick Howe (@Area9Nick @Area9Learning) talks about fabric of learning organization to bring #JobsOfFuture #Podcast

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

Sep 26, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Data shortage Source

[ AnalyticsWeek BYTES]

>> Rethinking classical approaches to analysis and predictive modeling by v1shal

>> A Practical Way to Approach Predictive Analytics Accuracy by analyticsweek

>> The Unexpected Connections Between Bitcoin and The Dow by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Data Mining

[ FEATURED READ]

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

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

Q:When would you use random forests Vs SVM and why?
A: * In a case of a multi-class classification problem: SVM will require one-against-all method (memory intensive)
* If one needs to know the variable importance (random forests can perform it as well)
* If one needs to get a model fast (SVM is long to tune, need to choose the appropriate kernel and its parameters, for instance sigma and epsilon)
* In a semi-supervised learning context (random forest and dissimilarity measure): SVM can work only in a supervised learning mode

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Big Data Analytics

Subscribe to Youtube

[ QUOTE OF THE WEEK]

Everybody gets so much information all day long that they lose their common sense. Gertrude Stein

[ PODCAST OF THE WEEK]

#FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

iTunes GooglePlay

[ FACT OF THE WEEK]

By then, our accumulated digital universe of data will grow from 4.4 zettabyets today to around 44 zettabytes, or 44 trillion gigabytes.

Sourced from: Analytics.CLUB #WEB Newsletter

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

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Insights Source

[ AnalyticsWeek BYTES]

>> Future of Public Sector and Jobs in #BigData World #FutureOfData #Podcast by v1shal

>> Black Friday is Becoming Irrelevant, How Retailers Should Survive by v1shal

>> Pascal Marmier (@pmarmier) @SwissRe discuss running data driven innovation catalyst by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

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]

Rise of the Robots: Technology and the Threat of a Jobless Future

What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence… 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:Provide examples of machine-to-machine communications?
A: Telemedicine
– Heart patients wear specialized monitor which gather information regarding heart state
– The collected data is sent to an electronic implanted device which sends back electric shocks to the patient for correcting incorrect rhythms

Product restocking
– Vending machines are capable of messaging the distributor whenever an item is running out of stock

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Keynote: The CMO isn’t satisfied: Judah Phillips

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]

Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

iTunes GooglePlay

[ FACT OF THE WEEK]

More than 5 billion people are calling, texting, tweeting and browsing on mobile phones worldwide.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 12, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Productivity Source

[ AnalyticsWeek BYTES]

>> Validating a Lostness Measure by analyticsweek

>> 6 Factors to Consider Before Building a Predictive Model for Life Insurance by analyticsweek

>> 7 Deadly Sins of Total Customer Experience by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

Intro to Machine Learning

Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most stra… more

[ FEATURED READ]

Data Science from Scratch: First Principles with Python

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]

[ DATA SCIENCE Q&A]

Product restocking
– Vending machines are capable of messaging the distributor whenever an item is running out of stock

Source

[ VIDEO OF THE WEEK]

@Schmarzo @DellEMC on Ingredients of healthy #DataScience practice #FutureOfData #Podcast

Subscribe to Youtube

[ QUOTE OF THE WEEK]

It’s easy to lie with statistics. It’s hard to tell the truth without statistics. Andrejs Dunkels

[ PODCAST OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

iTunes GooglePlay

[ FACT OF THE WEEK]

According to estimates, the volume of business data worldwide, across all companies, doubles every 1.2 years.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 05, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Fake data Source

[ AnalyticsWeek BYTES]

>> Big Data Insights in Healthcare, Part I. Great Ideas Transcend Time by froliol

>> Top 10 ways in which Google Analytics help online businesses by thomassujain

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

Wanna write? Click Here

[ FEATURED COURSE]

Tackle Real Data Challenges

Learn scalable data management, evaluate big data technologies, and design effective visualizations…. more

[ FEATURED READ]

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

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]

[ DATA SCIENCE Q&A]

Q:Explain what a local optimum is and why it is important in a specific context,
such as K-means clustering. What are specific ways of determining if you have a local optimum problem? What can be done to avoid local optima?
A: * A solution that is optimal in within a neighboring set of candidate solutions
* In contrast with global optimum: the optimal solution among all others

* K-means clustering context:
Its proven that the objective cost function will always decrease until a local optimum is reached.
Results will depend on the initial random cluster assignment

* Determining if you have a local optimum problem:
Tendency of premature convergence
Different initialization induces different optima

* Avoid local optima in a K-means context: repeat K-means and take the solution that has the lowest cost

Source

[ VIDEO OF THE WEEK]

Understanding How Fitness Tracker Works via @SciThinkers #STEM #STEAM

Subscribe to Youtube

[ QUOTE OF THE WEEK]

Numbers have an important story to tell. They rely on you to give them a voice. Stephen Few

[ PODCAST OF THE WEEK]

@JustinBorgman on Running a data science startup, one decision at a time #Futureofdata #Podcast

iTunes GooglePlay

[ FACT OF THE WEEK]

Market research firm IDC has released a new forecast that shows the big data market is expected to grow from $3.2 billion in 2010 to $16.9 billion in 2015.

Sourced from: Analytics.CLUB #WEB Newsletter

Aug 29, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Data interpretation Source

[ LOCAL EVENTS & SESSIONS]

Sep 14, 2019 #WEB Intro R Webinar: R Basics, Data Exploration, & R Operators
Sep 05, 2019 #WEB Intro to Data Science: Predictive Modeling
Sep 10, 2019 #WEB Intro to Python: Fundamentals

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Landscape of Big Data by v1shal

>> The Future of Big Data? Three Use Cases of Prescriptive Analytics by analyticsweekpick

>> Data Monetization Workshop 2018: Key Themes & Takeaways by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Artificial Intelligence

This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances…. more

[ FEATURED READ]

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for e… more

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

Q:What is cross-validation? How to do it right?
A: It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set.

Examples: leave-one-out cross validation, K-fold cross validation

How to do it right?

the training and validation data sets have to be drawn from the same population
predicting stock prices: trained for a certain 5-year period, it’s unrealistic to treat the subsequent 5-year a draw from the same population
common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well
Bias-variance trade-off for k-fold cross validation:

Leave-one-out cross-validation: gives approximately unbiased estimates of the test error since each training set contains almost the entire data set (n?1n?1 observations).

But: we average the outputs of n fitted models, each of which is trained on an almost identical set of observations hence the outputs are highly correlated. Since the variance of a mean of quantities increases when correlation of these quantities increase, the test error estimate from a LOOCV has higher variance than the one obtained with k-fold cross validation

Typically, we choose k=5 or k=10, as these values have been shown empirically to yield test error estimates that suffer neither from excessively high bias nor high variance.
Source

[ VIDEO OF THE WEEK]

@BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

Subscribe to Youtube

[ QUOTE OF THE WEEK]

Data matures like wine, applications like fish. James Governor

[ PODCAST OF THE WEEK]

#GlobalBusiness at the speed of The #BigAnalytics

iTunes GooglePlay

[ FACT OF THE WEEK]

140,000 to 190,000. Too few people with deep analytical skills to fill the demand of Big Data jobs in the U.S. by 2018.

Sourced from: Analytics.CLUB #WEB Newsletter

Aug 22, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Data security Source

[ LOCAL EVENTS & SESSIONS]

Oct 12, 2019 #WEB Intro R Webinar: Data Types, Data Structures, & Row/Column Binding
Oct 01, 2019 #WEB Global Artificial Intelligence Conference Boston October 2019
Aug 29, 2019 #WEB Dumb & Dumber vs Oceanâs 11: Tackling evolving, sophisticated fraud with AI

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Secret Sauce to Sustain Business through Technology Driven Era by d3eksha

>> Join View from the Top 500 (VFT-500) to Share with and Learn from your CEM Peers by bobehayes

>> âPutting Data Everywhereâ: Leveraging Centralized Business Intelligence for Full-Blown Data Culture by jelaniharper

Wanna write? Click Here

[ FEATURED COURSE]

Applied Data Science: An Introduction

[ FEATURED READ]

Big Data: A Revolution That Will Transform How We Live, Work, and Think

Illuminating and very timely . . . a fascinating and sometimes alarming survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… more

[ TIPS & TRICKS OF THE WEEK]

Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ DATA SCIENCE Q&A]

Q:How do you control for biases?
A: * Choose a representative sample, preferably by a random method
* Choose an adequate size of sample
* Identify all confounding factors if possible
* Identify sources of bias and include them as additional predictors in statistical analyses
* Use randomization: by randomly recruiting or assigning subjects in a study, all our experimental groups have an equal chance of being influenced by the same bias

Notes:
– Randomization: in randomized control trials, research participants are assigned by chance, rather than by choice to either the experimental group or the control group.
– Random sampling: obtaining data that is representative of the population of interest

Source

[ VIDEO OF THE WEEK]

Pascal Marmier (@pmarmier) @SwissRe discusses running data driven innovation catalyst

Subscribe to Youtube

[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

#DataScience Approach to Reducing #Employee #Attrition

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

Aug 15, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Complex data Source

[ LOCAL EVENTS & SESSIONS]

Sep 05, 2019 #WEB Thinkful Webinar | Becoming a Data Analyst Info Session
Aug 20, 2019 #WEB Call for presenters: Government Advances Statistical Programming (GASP!) Workshp
Aug 24, 2019 #WEB AWS Certified Solutions Architect – Associate Study Group

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> The real-time machine for every business: Big data-driven market analytics by thomassujain

>> Data Virtualization: A Spectrum of Approaches â A GigaOm Market Landscape Report by analyticsweekpick

>> An Agile Approach to Big Data Management: Intelligent Workload Routing Automation by jelaniharper

Wanna write? Click Here

[ FEATURED COURSE]

Introduction to Apache Spark

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

[ FEATURED READ]

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

Q:What is better: good data or good models? And how do you define ‘good? Is there a universal good model? Are there any models that are definitely not so good?
A: * Good data is definitely more important than good models
* If quality of the data wasnt of importance, organizations wouldnt spend so much time cleaning and preprocessing it!
* Even for scientific purpose: good data (reflected by the design of experiments) is very important

How do you define good?
– good data: data relevant regarding the project/task to be handled
– good model: model relevant regarding the project/task
– good model: a model that generalizes on external data sets

Is there a universal good model?
– No, otherwise there wouldnt be the overfitting problem!
– Algorithm can be universal but not the model
– Model built on a specific data set in a specific organization could be ineffective in other data set of the same organization
– Models have to be updated on a somewhat regular basis

Are there any models that are definitely not so good?
– ‘all models are wrong but some are useful George E.P. Box
– It depends on what you want: predictive models or explanatory power
– If both are bad: bad model

Source

[ VIDEO OF THE WEEK]

@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

Subscribe to Youtube

[ QUOTE OF THE WEEK]

Processed data is information. Processed information is knowledge Processed knowledge is Wisdom. – Ankala V. Subbarao

[ PODCAST OF THE WEEK]

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

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.