Jul 12, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Big Data knows everything  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

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

>> Free Research Report on the State of Patient Experience in US Hospitals by bobehayes

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

Wanna write? Click Here

[ NEWS BYTES]

>>
 Bill Promoting Behavioral Health EHR Incentives Passes House – EHRIntelligence.com Under  Health Analytics

>>
 India sees 77% growth in HR analytics professionals in 5 years, says report – Business Standard Under  Talent Analytics

>>
 Utilizing Healthcare Data Security, Cloud for a Stronger Environment – HealthITSecurity.com Under  Cloud Security

More NEWS ? Click Here

[ FEATURED COURSE]

Tackle Real Data Challenges

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

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

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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. The… 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: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
* Zipf’s 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]

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

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

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]

@ChuckRehberg / @TrigentSoftware on Translating Technology to Solve Business Problems #FutureOfData #Podcast

 @ChuckRehberg / @TrigentSoftware on Translating Technology to Solve Business Problems #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

40% projected growth in global data generated per year vs. 5% growth in global IT spending.

Sourced from: Analytics.CLUB #WEB Newsletter

Jul 05, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Correlation-Causation  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Why the time is ripe for security behaviour analytics by analyticsweekpick

>> #FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise by v1shal

>> CMS Predictive Readmission Models ‘Not Very Good’ by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Global BPO Business Analytics Market by 2023: Accenture, HP, TCS … – Healthcare Journal Under  Business Analytics

>>
 Streaming Analytics Market: Business Opportunities, Current Trends, Market Challenges & Global Industry Analysis By … – Business Investor Under  Streaming Analytics

>>
 Microsoft Azure Container Instances Now Production-Ready – Virtualization Review Under  Virtualization

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 Black Swan: The Impact of the Highly Improbable

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A black swan is an event, positive or negative, that is deemed improbable yet causes massive consequences. In this groundbreaking and prophetic book, Taleb shows in a playful way that Black Swan events explain almost eve… 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:What is principal component analysis? Explain the sort of problems you would use PCA for. Also explain its limitations as a method?

A: Statistical method that uses an orthogonal transformation to convert a set of observations of correlated variables into a set of values of linearly uncorrelated variables called principal components.

Reduce the data from n to k dimensions: find the k vectors onto which to project the data so as to minimize the projection error.
Algorithm:
1) Preprocessing (standardization): PCA is sensitive to the relative scaling of the original variable
2) Compute covariance matrix ?
3) Compute eigenvectors of ?
4) Choose kk principal components so as to retain xx% of the variance (typically x=99)

Applications:
1) Compression
– Reduce disk/memory needed to store data
– Speed up learning algorithm. Warning: mapping should be defined only on training set and then applied to test set

2. Visualization: 2 or 3 principal components, so as to summarize data

Limitations:
– PCA is not scale invariant
– The directions with largest variance are assumed to be of most interest
– Only considers orthogonal transformations (rotations) of the original variables
– PCA is only based on the mean vector and covariance matrix. Some distributions (multivariate normal) are characterized by this but some are not
– If the variables are correlated, PCA can achieve dimension reduction. If not, PCA just orders them according to their variances

Source

[ VIDEO OF THE WEEK]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The most valuable commodity I know of is information. – Gordon Gekko

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Big data is a top business priority and drives enormous opportunity for business improvement. Wikibon’s own study projects that big data will be a $50 billion business by 2017.

Sourced from: Analytics.CLUB #WEB Newsletter

Jun 28, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Accuracy  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Enterprise Architecture for the Internet of Things: Containerization and Microservices by jelaniharper

>> 25 Hilarious Geek Quotes for Geek-Wear by v1shal

>> May 22, 2017 Health and Biotech analytics news roundup by pstein

Wanna write? Click Here

[ NEWS BYTES]

>>
 What the country’s first undergrad program in artificial intelligence will look like – EdScoop News Under  Artificial Intelligence

>>
 Rugby Statistics: Cooney’s figures still stand up to scrutiny – Irish Times Under  Statistics

>>
 Witad Awards 2018 Write-Ups: Data Scientist of the Year … – www.waterstechnology.com Under  Data Scientist

More NEWS ? Click Here

[ FEATURED COURSE]

The Analytics Edge

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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]

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]

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: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:
It’s 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]

@ReshanRichards on creating a learning startup for preparing for #FutureOfWork #JobsOfFuture #Podcast

 @ReshanRichards on creating a learning startup for preparing for #FutureOfWork #JobsOfFuture #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Without big data, you are blind and deaf and in the middle of a freeway. – Geoffrey Moore

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

29 percent report that their marketing departments have ‘too little or no customer/consumer data.’ When data is collected by marketers, it is often not appropriate to real-time decision making.

Sourced from: Analytics.CLUB #WEB Newsletter

Jun 21, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Ethics  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Enterprise Data Modeling Made Easy by jelaniharper

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

>> Mar 01, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

Wanna write? Click Here

[ NEWS BYTES]

>>
 Network Intelligence witnesses 48% growth in FY 2017-18 – Tech Observer Under  Big Data Security

>>
 Predictive and Prescriptive Analytics Market valued of USD 16.84 billion, at a CAGR of 20.43% by the end of 2023 – The Financial Analyst Under  Prescriptive Analytics

>>
 New York City Spending the Focus at Fourth Annual Business Analytics Conference – Manhattan College News Under  Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Deep Learning Prerequisites: The Numpy Stack in Python

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

[ FEATURED READ]

The Misbehavior of Markets: A Fractal View of Financial Turbulence

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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]

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: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]

@AnalyticsWeek Panel Discussion: Health Informatics Analytics

 @AnalyticsWeek Panel Discussion: Health Informatics Analytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

What we have is a data glut. – Vernon Vinge

[ PODCAST OF THE WEEK]

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

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

Subscribe 

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[ FACT OF THE WEEK]

The data volumes are exploding, more data has been created in the past two years than in the entire previous history of the human race.

Sourced from: Analytics.CLUB #WEB Newsletter

Jun 14, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Productivity  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> What is data security For Organizations? by analyticsweekpick

>> Data Modeling Tomorrow: Self-Describing Data Formats by jelaniharper

>> Data-As-A-Service to enable compliance reporting by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 Amazon’s cloud business is using this weird 1976 ‘Saturday Night Live’ skit to explain its new blockchain product – Business Insider Under  Cloud

>>
 Virtualization Software Market Overview, Cost Structure Analysis, Growth Opportunities and Forecast to 2023 – Exclusive Reportage Under  Virtualization

>>
 Stellar Lumens [XLM], thrusters burned out? – Sentiment Analysis – April 16 – AMBCrypto Under  Sentiment Analysis

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]

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:What is: collaborative filtering, n-grams, cosine distance?
A: Collaborative filtering:
– Technique used by some recommender systems
– Filtering for information or patterns using techniques involving collaboration of multiple agents: viewpoints, data sources.
1. A user expresses his/her preferences by rating items (movies, CDs.)
2. The system matches this user’s ratings against other users’ and finds people with most similar tastes
3. With similar users, the system recommends items that the similar users have rated highly but not yet being rated by this user

n-grams:
– Contiguous sequence of n items from a given sequence of text or speech
– ‘Andrew is a talented data scientist”
– Bi-gram: ‘Andrew is”, ‘is a”, ‘a talented”.
– Tri-grams: ‘Andrew is a”, ‘is a talented”, ‘a talented data”.
– An n-gram model models sequences using statistical properties of n-grams; see: Shannon Game
– More concisely, n-gram model: P(Xi|Xi?(n?1)…Xi?1): Markov model
– N-gram model: each word depends only on the n?1 last words

Issues:
– when facing infrequent n-grams
– solution: smooth the probability distributions by assigning non-zero probabilities to unseen words or n-grams
– Methods: Good-Turing, Backoff, Kneser-Kney smoothing

Cosine distance:
– How similar are two documents?
– Perfect similarity/agreement: 1
– No agreement : 0 (orthogonality)
– Measures the orientation, not magnitude

Given two vectors A and B representing word frequencies:
cosine-similarity(A,B)=?A,B?/||A||?||B||

Source

[ VIDEO OF THE WEEK]

Making sense of unstructured data by turning strings into things

 Making sense of unstructured data by turning strings into things

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

#BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

 #BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

Subscribe 

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[ FACT OF THE WEEK]

Big data is a top business priority and drives enormous opportunity for business improvement. Wikibon’s own study projects that big data will be a $50 billion business by 2017.

Sourced from: Analytics.CLUB #WEB Newsletter

Jun 07, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Tour of Accounting  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> December 5, 2016 Health and Biotech analytics news roundup by pstein

>> Is data analytics about causes … or correlations? by analyticsweekpick

>> Mar 15, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

Wanna write? Click Here

[ NEWS BYTES]

>>
 Getting Data Science Right: How To Structure Data Science Teams For Maximum Results – Forbes Under  Data Science

>>
 4 Companies Betting on Cloud Gaming – Motley Fool Under  Cloud

>>
 From Hawkeye country to the Vikings with data analytics – The Gazette: Eastern Iowa Breaking News and Headlines Under  Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Deep Learning Prerequisites: The Numpy Stack in Python

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The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence… 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:What is the Central Limit Theorem? Explain it. Why is it important?
A: The CLT states that the arithmetic mean of a sufficiently large number of iterates of independent random variables will be approximately normally distributed regardless of the underlying distribution. i.e: the sampling distribution of the sample mean is normally distributed.
– Used in hypothesis testing
– Used for confidence intervals
– Random variables must be iid: independent and identically distributed
– Finite variance

Source

[ VIDEO OF THE WEEK]

#FutureOfData with Rob(@telerob) / @ConnellyAgency on running innovation in agency

 #FutureOfData with Rob(@telerob) / @ConnellyAgency on running innovation in agency

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The temptation to form premature theories upon insufficient data is the bane of our profession. – Sherlock Holmes

[ PODCAST OF THE WEEK]

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

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

Subscribe 

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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

May 31, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Convincing  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Understanding the Mobile User Experience Will Help you Build a Better App by bobehayes

>> BOB in The Netherlands by bobehayes

>> The Practice of Customer Experience Management: An Overview by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 North Hilo Crime Statistics for April – Big Island Now Under  Statistics

>>
 Hadoop & Big Data Analytics Market 2017-2024 Research Report – Technical Progress Under  Hadoop

>>
 Real-time Analytics Requires Modern IT Infrastructure – RTInsights (press release) (blog) Under  Streaming Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Baseball Data Wrangling with Vagrant, R, and Retrosheet

image

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

[ FEATURED READ]

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

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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]

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:Provide a simple example of how an experimental design can help answer a question about behavior. How does experimental data contrast with observational data?
A: * You are researching the effect of music-listening on studying efficiency
* You might divide your subjects into two groups: one would listen to music and the other (control group) wouldn’t listen anything!
* You give them a test
* Then, you compare grades between the two groups

Differences between observational and experimental data:
– Observational data: measures the characteristics of a population by studying individuals in a sample, but doesn’t attempt to manipulate or influence the variables of interest
– Experimental data: applies a treatment to individuals and attempts to isolate the effects of the treatment on a response variable

Observational data: find 100 women age 30 of which 50 have been smoking a pack a day for 10 years while the other have been smoke free for 10 years. Measure lung capacity for each of the 100 women. Analyze, interpret and draw conclusions from data.

Experimental data: find 100 women age 20 who don’t currently smoke. Randomly assign 50 of the 100 women to the smoking treatment and the other 50 to the no smoking treatment. Those in the smoking group smoke a pack a day for 10 years while those in the control group remain smoke free for 10 years. Measure lung capacity for each of the 100 women.
Analyze, interpret and draw conclusions from data.

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Big Data Analytics

 @AnalyticsWeek Panel Discussion: Big Data Analytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Information is the oil of the 21st century, and analytics is the combustion engine. – Peter Sondergaard

[ PODCAST OF THE WEEK]

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

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

Subscribe 

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[ FACT OF THE WEEK]

Every person in the world having more than 215m high-resolution MRI scans a day.

Sourced from: Analytics.CLUB #WEB Newsletter

May 24, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data interpretation  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ NEWS BYTES]

>>
 The case for one giant, multibillion-dollar cloud contract for DoD – C4ISRNet Under  Cloud

>>
 Streaming Analytics Market Is Constantly Growing On Account Of the Increasing Operational Efficiency And Production … – Expert Consulting Under  Streaming Analytics

>>
 ‘Salah’s statistics in his debut season at Anfield are quite astonishing’ – how the papers saw Liverpool FC’s … – Daily Post North Wales Under  Statistics

More NEWS ? Click Here

[ FEATURED COURSE]

Tackle Real Data Challenges

image

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

image

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:Explain Tufte’s concept of ‘chart junk’?
A: All visuals elements in charts and graphs that are not necessary to comprehend the information represented, or that distract the viewer from this information

Examples of unnecessary elements include:
– Unnecessary text
– Heavy or dark grid lines
– Ornamented chart axes
– Pictures
– Background
– Unnecessary dimensions
– Elements depicted out of scale to one another
– 3-D simulations in line or bar charts

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data at Work: Paul Sonderegger

 @AnalyticsWeek: Big Data at Work: Paul Sonderegger

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[ QUOTE OF THE WEEK]

With data collection, ‘the sooner the better’ is always the best answer. – Marissa Mayer

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

29 percent report that their marketing departments have ‘too little or no customer/consumer data.’ When data is collected by marketers, it is often not appropriate to real-time decision making.

Sourced from: Analytics.CLUB #WEB Newsletter

May 17, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Big Data knows everything  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Customer Service Excellence in 8 steps! by martin

>> Underpinning Enterprise Data Governance with Machine Intelligence by jelaniharper

>> Navigating Big Data Careers with a Statistics PhD by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Startup Dremio Promises Improved Big Data Access For Business Analytics With New Release – CRN Under  Business Analytics

>>
 Teladoc taps IBM Watson machine learning for second opinion service – Healthcare IT News Under  Machine Learning

>>
 Amazon or no, banks are in for big changes, one analyst says … – MarketWatch Under  Financial 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]

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 do you think about the idea of injecting noise in your data set to test the sensitivity of your models?
A: * Effect would be similar to regularization: avoid overfitting
* Used to increase robustness

Source

[ VIDEO 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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[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST OF THE WEEK]

@ChuckRehberg / @TrigentSoftware on Translating Technology to Solve Business Problems #FutureOfData #Podcast

 @ChuckRehberg / @TrigentSoftware on Translating Technology to Solve Business Problems #FutureOfData #Podcast

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[ FACT OF THE WEEK]

73% of organizations have already invested or plan to invest in big data by 2016

Sourced from: Analytics.CLUB #WEB Newsletter

May 10, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Pacman  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Dec 21, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> â€œPutting Data Everywhere”: Leveraging Centralized Business Intelligence for Full-Blown Data Culture by jelaniharper

>> Finance Best Practices Are Changing—Is Your Organization Keeping Pace? by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 6 Tips for Keeping Iot Devices Safe – Security Sales & Integration Under  IOT

>>
 Software AG ramps up Australian push with IoT platform – IoT Hub Under  Streaming Analytics

>>
 Customer experience in a new dimension: 3D Augmented Reality App Mercedes cAR and Virtual Reality goggles … – Automotive World (press release) Under  Customer Experience

More NEWS ? Click Here

[ FEATURED COURSE]

Python for Beginners with Examples

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A practical Python course for beginners with examples and exercises…. 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]

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:What is star schema? Lookup tables?
A: The star schema is a traditional database schema with a central (fact) table (the “observations”, with database “keys” for joining with satellite tables, and with several fields encoded as ID’s). Satellite tables map ID’s to physical name or description and can be “joined” to the central fact table using the ID fields; these tables are known as lookup tables, and are particularly useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve multiple layers of summarization (summary tables, from granular to less granular) to retrieve information faster.

Lookup tables:
– Array that replace runtime computations with a simpler array indexing operation

Source

[ VIDEO OF THE WEEK]

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

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

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[ QUOTE OF THE WEEK]

War is 90% information. – Napoleon Bonaparte

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

73% of organizations have already invested or plan to invest in big data by 2016

Sourced from: Analytics.CLUB #WEB Newsletter