Category: Uncategorized

[ 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

Aug 08, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Trust the data Source

[ LOCAL EVENTS & SESSIONS]

Aug 12, 2019 #WEB Hadoop vs Spark – Demystifying the Difference (Online Webinar)
Sep 10, 2019 #WEB Webinar – Introduction to Power BI for Business Professionals
Aug 27, 2019 #WEB IndyPy Bytes: From Tkinter to Bottle

>> Inside CXM: New Global Thought Leader Hub for Customer Experience Professionals by bobehayes

[ AnalyticsWeek BYTES]

>> The 4 Common Challenges of Predictive Analytics by analyticsweek

>> What Motivates People to Take Free Surveys? by analyticsweek

Deep Learning Prerequisites: The Numpy Stack in Python

[ FEATURED COURSE]

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence… more

[ FEATURED READ]

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th Edition

The eagerly anticipated Fourth Edition of the title that pioneered the comparison of qualitative, quantitative, and mixed methods research design is here! For all three approaches, Creswell includes a preliminary conside… 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 cant 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]

Want to fix #DataScience ? fix #governance by @StephenGatchell @Dell #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

Torture the data, and it will confess to anything. Ronald Coase

[ PODCAST OF THE WEEK]

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

[ FACT OF THE WEEK]

Bad data or poor data quality costs US businesses $600 billion annually.

Sourced from: Analytics.CLUB #WEB Newsletter

Aug 01, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Data security Source

[ LOCAL EVENTS & SESSIONS]

Aug 15, 2019 #WEB Webinar: What’s New in H2O Driverless AI
Aug 07, 2019 #WEB Webinar on Azure Data Lake Analytics
Aug 22, 2019 #WEB [AgensGraph Webinar] IMDB in Graph Database

>> Using Task Ease (SEQ) to Predict Completion Rates and Times by analyticsweek

[ AnalyticsWeek BYTES]

>> October 24, 2016 Health and Biotech analytics news roundup by pstein

>> Up Your Game With Interactive Data Visualizations by analyticsweek

Introduction to Apache Spark

[ FEATURED COURSE]

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

[ FEATURED READ]

Storytelling with Data: A Data Visualization Guide for Business Professionals

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 SCIENCE Q&A]

Q:How do you know if one algorithm is better than other?
A: * In terms of performance on a given data set?
* In terms of performance on several data sets?
* In terms of efficiency?
In terms of performance on several data sets:

– ‘Does learning algorithm A have a higher chance of producing a better predictor than learning algorithm B in the given context?
– ‘Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets, A. Lacoste and F. Laviolette
– ‘Statistical Comparisons of Classifiers over Multiple Data Sets, Janez Demsar

In terms of performance on a given data set:
– One wants to choose between two learning algorithms
– Need to compare their performances and assess the statistical significance

One approach (Not preferred in the literature):
– Multiple k-fold cross validation: run CV multiple times and take the mean and sd
– You have: algorithm A (mean and sd) and algorithm B (mean and sd)
– Is the difference meaningful? (Paired t-test)

Sign-test (classification context):
Simply counts the number of times A has a better metrics than B and assumes this comes from a binomial distribution. Then we can obtain a p-value of the HoHo test: A and B are equal in terms of performance.

Wilcoxon signed rank test (classification context):
Like the sign-test, but the wins (A is better than B) are weighted and assumed coming from a symmetric distribution around a common median. Then, we obtain a p-value of the HoHo test.

Other (without hypothesis testing):
– AUC
– F-Score

Source

[ VIDEO OF THE WEEK]

Data-As-A-Service (#DAAS) to enable compliance reporting

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[ QUOTE OF THE WEEK]

Data really powers everything that we do. Jeff Weiner

[ PODCAST OF THE WEEK]

@EdwardBoudrot / @Optum on #DesignThinking & #DataDriven Products #FutureOfData #Podcast

[ 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 25, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Fake data Source

[ LOCAL EVENTS & SESSIONS]

Aug 08, 2019 #WEB Thinkful Webinar | Getting Started in Data Science
Aug 14, 2019 #WEB Intro to Python: Fundamentals
Aug 10, 2019 #WEB Google AI Workshop: Machine learning with Tensorflow

>> Big Data Is No Longer Confined to the Big Business Playbook by analyticsweekpick

[ AnalyticsWeek BYTES]

>> Are You Evolving Your Analytics? by analyticsweek

>> Meet Us in DC for the 2019 Logi Conference by analyticsweek

Applied Data Science: An Introduction

[ FEATURED COURSE]

As the world’s data grow exponentially, organizations across all sectors, including government and not-for-profit, need to understand, manage and use big, complex data setsknown as big data…. more

[ FEATURED READ]

Thinking, Fast and Slow

Drawing on decades of research in psychology that resulted in a Nobel Prize in Economic Sciences, Daniel Kahneman takes readers on an exploration of what influences thought example by example, sometimes with unlikely wor… 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 difference between supervised learning and unsupervised learning? Give concrete examples
?
A: * Supervised learning: inferring a function from labeled training data
* Supervised learning: predictor measurements associated with a response measurement; we wish to fit a model that relates both for better understanding the relation between them (inference) or with the aim to accurately predicting the response for future observations (prediction)
* Supervised learning: support vector machines, neural networks, linear regression, logistic regression, extreme gradient boosting
* Supervised learning examples: predict the price of a house based on the are, size.; churn prediction; predict the relevance of search engine results.
* Unsupervised learning: inferring a function to describe hidden structure of unlabeled data
* Unsupervised learning: we lack a response variable that can supervise our analysis
* Unsupervised learning: clustering, principal component analysis, singular value decomposition; identify group of customers
* Unsupervised learning examples: find customer segments; image segmentation; classify US senators by their voting.

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

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[ QUOTE OF THE WEEK]

The world is one big data problem. – Andrew McAfee

[ PODCAST OF THE WEEK]

#FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

[ 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

Jul 18, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Data shortage Source

[ LOCAL EVENTS & SESSIONS]

Aug 10, 2019 #WEB AWS Certified Developer – Associate Study Group
Aug 07, 2019 #WEB Free Crash Course | Data Analytics
Aug 08, 2019 #WEB Data Engineering & Analytics Engineering -Python, DB and App.Devs-Global Webinar

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

[ AnalyticsWeek BYTES]

>> Are APIs becoming the keys to customer experience? by analyticsweekpick

>> Could these 5 big data projects stop climate change? by analyticsweekpick

Process Mining: Data science in Action

[ FEATURED COURSE]

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]

The Misbehavior of Markets: A Fractal View of Financial Turbulence

Mathematical superstar and inventor of fractal geometry, Benoit Mandelbrot, has spent the past forty years studying the underlying mathematics of space and natural patterns. What many of his followers don’t realize is th… more

[ TIPS & TRICKS OF THE WEEK]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:Explain selection bias (with regard to a dataset, not variable selection). Why is it important? How can data management procedures such as missing data handling make it worse?
A: * Selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved
Types:
– Sampling bias: systematic error due to a non-random sample of a population causing some members to be less likely to be included than others
– Time interval: a trial may terminated early at an extreme value (ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all the variables have similar means
– Data: cherry picking, when specific subsets of the data are chosen to support a conclusion (citing examples of plane crashes as evidence of airline flight being unsafe, while the far more common example of flights that complete safely)
– Studies: performing experiments and reporting only the most favorable results
– Can lead to unaccurate or even erroneous conclusions
– Statistical methods can generally not overcome it

Why data handling make it worse?
– Example: individuals who know or suspect that they are HIV positive are less likely to participate in HIV surveys
– Missing data handling will increase this effect as its based on most HIV negative
-Prevalence estimates will be unaccurate

Source

[ VIDEO OF THE WEEK]

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

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[ QUOTE OF THE WEEK]

You can have data without information, but you cannot have information without data. – Daniel Keys Moran

[ PODCAST OF THE WEEK]

@AlexWG on Unwrapping Intelligence in #ArtificialIntelligence #FutureOfData #Podcast

[ FACT OF THE WEEK]

In the developed economies of Europe, government administrators could save more than 100 billion ($149 billion) in operational efficiency improvements alone by using big data, not including using big data to reduce fraud and errors and boost the collection of tax revenues.

Sourced from: Analytics.CLUB #WEB Newsletter

Jul 11, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Statistics Source

[ LOCAL EVENTS & SESSIONS]

Jul 22, 2019 #WEB Lean Six Sigma Black Belt (LSSBB) Certification Training in Melbourne, FL
Jul 23, 2019 #WEB Lean Six Sigma Green Belt (LSSGB) Certification Training in Austin, TX
Jul 19, 2019 #WEB Construindo uma Arquitetura Centrada em Dados (Data-Centric)

[ AnalyticsWeek BYTES]

>> Aureus at InsureTech Connect 2017, Las Vegas by analyticsweek

>> Are You Headed for the Analytics Cliff? by analyticsweek

>> Big data: The critical ingredient by analyticsweekpick

Deep Learning Prerequisites: The Numpy Stack in Python

[ FEATURED COURSE]

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence… 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]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:What is the Law of Large Numbers?
A: * A theorem that describes the result of performing the same experiment a large number of times
* Forms the basis of frequency-style thinking
* It says that the sample mean, the sample variance and the sample standard deviation converge to what they are trying to estimate
* Example: roll a dice, expected value is 3.5. For a large number of experiments, the average converges to 3.5

Source

[ VIDEO OF THE WEEK]

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

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

#FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

[ FACT OF THE WEEK]

571 new websites are created every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

Jul 04, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Fake data Source

[ LOCAL EVENTS & SESSIONS]

Jul 13, 2019 #WEB XLNet: A New NLP Method that outperforms BERT
Jul 10, 2019 #WEB Webinar: Shining a Light on the Opioid Epidemic with the Power of Analytics
Jul 12, 2019 #WEB Free Big Data Hadoop and Spark Bootcamp – Menlo Park CA

Pattern Discovery in Data Mining

[ FEATURED COURSE]

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern disc… more

[ FEATURED READ]

The Misbehavior of Markets: A Fractal View of Financial Turbulence

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

Q:Explain what a long-tailed distribution is and provide three examples of relevant phenomena that have long tails. Why are they important in classification and regression problems?
A: * In long tailed distributions, a high frequency population is followed by a low frequency population, which gradually tails off asymptotically
* Rule of thumb: majority of occurrences (more than half, and when Pareto principles applies, 80%) are accounted for by the first 20% items in the distribution
* The least frequently occurring 80% of items are more important as a proportion of the total population
* Zipfs law, Pareto distribution, power laws

Examples:
1) Natural language
– Given some corpus of natural language – The frequency of any word is inversely proportional to its rank in the frequency table
– The most frequent word will occur twice as often as the second most frequent, three times as often as the third most frequent
– The accounts for 7% of all word occurrences (70000 over 1 million)
– ‘of accounts for 3.5%, followed by ‘and
– Only 135 vocabulary items are needed to account for half the English corpus!

2. Allocation of wealth among individuals: the larger portion of the wealth of any society is controlled by a smaller percentage of the people

3. File size distribution of Internet Traffic

Additional: Hard disk error rates, values of oil reserves in a field (a few large fields, many small ones), sizes of sand particles, sizes of meteorites

Importance in classification and regression problems:
– Skewed distribution
– Which metrics to use? Accuracy paradox (classification), F-score, AUC
– Issue when using models that make assumptions on the linearity (linear regression): need to apply a monotone transformation on the data (logarithm, square root, sigmoid function)
– Issue when sampling: your data becomes even more unbalanced! Using of stratified sampling of random sampling, SMOTE (‘Synthetic Minority Over-sampling Technique, NV Chawla) or anomaly detection approach

Source

[ VIDEO OF THE WEEK]

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

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[ QUOTE OF THE WEEK]

You can have data without information, but you cannot have information without data. – Daniel Keys Moran

[ PODCAST OF THE WEEK]

@JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast

[ 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 27, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Accuracy Source

[ LOCAL EVENTS & SESSIONS]

Aug 01, 2019 #WEB (Webinar) Evolution of the Data Warehouse
Jul 01, 2019 #WEB Webinar â Web Edit: The Power of Tableau in the Browser
Jul 12, 2019 #WEB Free Big Data Hadoop and Spark Bootcamp – Los Altos CA

>> Three Types Of Context To Make Your Audience Care About Your Data by analyticsweek

[ AnalyticsWeek BYTES]

>> Tackling 4th Industrial Revolution with HR4.0 by v1shal

>> Remote DBA Experts- Improve Business Intelligence with The Perfect Analytical Experts by thomassujain

Learning from data: Machine learning course

[ FEATURED COURSE]

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]

Introduction to Graph Theory (Dover Books on Mathematics)

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 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]

@DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

What we have is a data glut. Vernon Vinge

[ PODCAST OF THE WEEK]

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

[ 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

Jun 20, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Insights Source

[ LOCAL EVENTS & SESSIONS]

Jul 11, 2019 #WEB WEBINAR – Aligning Product and Sales with Shared Metrics – Google + Looker + Fivetran
Jun 23, 2019 #WEB How to Become Data Science And Artificial Intelligence Expert By Learnbay?
Jun 27, 2019 #WEB Online meetup: Inside Snowflake

>> How Airbnb Uses Big Data And Machine Learning To Guide Hosts To The Perfect Price by analyticsweekpick

[ AnalyticsWeek BYTES]

>> My Conversation with Oracle on Customer Experience Management by bobehayes

>> Accelerating Discovery with a Unified Analytics Platform for Genomics by analyticsweek

R Basics – R Programming Language Introduction

[ FEATURED COURSE]

Learn the essentials of R Programming – R Beginner Level!… 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 SCIENCE Q&A]

Q:Is mean imputation of missing data acceptable practice? Why or why not?
A: * Bad practice in general
* If just estimating means: mean imputation preserves the mean of the observed data
* Leads to an underestimate of the standard deviation
* Distorts relationships between variables by pulling estimates of the correlation toward zero

Source

[ VIDEO OF THE WEEK]

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

Subscribe to Youtube

[ QUOTE OF THE WEEK]

For every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%. Andrew Hogue, Foursquare

[ PODCAST OF THE WEEK]

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

[ 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

Jun 13, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Data shortage Source

[ LOCAL EVENTS & SESSIONS]

Jun 26, 2019 #WEB Webinar. Building Explainable Models with Auger.AI
Jun 20, 2019 #WEB IndiaBiostreams: “What is IndiaBioscience?”
Jun 17, 2019 #WEB TensorFlow + Swift + OpenAI’s Unsupervised Sentiment, KubeFlow, TFX, Kubernetes

>> @DarrWest / @BrookingsInst on the Future of Work: AI, Robots & Automation #JobsOfFuture by v1shal

[ AnalyticsWeek BYTES]

>> Geeks Vs Nerds [Infographics] by v1shal

>> User Experience Salaries & Calculator (2018) by analyticsweek