Nov 15, 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]

>> 6 Big Data Analytics Use Cases for Healthcare IT by analyticsweekpick

>> Sisense Hunch™ – Leadership Through Radical Innovation by analyticsweek

>> Next-generation supply & demand forecasting: How machine learning is helping retailers to save millions by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Data Is The Foundation For Artificial Intelligence And Machine Learning – Forbes Under  Machine Learning

>>
 How to Get Into “Internet of Things” Investments – Banyan Hill Publishing Under  Internet Of Things

>>
 Beyond Big Data: The extreme data economy – Networks Asia Under  Big Data

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 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]

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: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 it’s based on most HIV negative
-Prevalence estimates will be unaccurate

Source

[ VIDEO OF THE WEEK]

Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #FutureOfWork #JobsOfFuture #Podcast

 Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #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]

Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

 Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

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[ FACT OF THE WEEK]

Retailers who leverage the full power of big data could increase their operating margins by as much as 60%.

Sourced from: Analytics.CLUB #WEB Newsletter

Nov 08, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data security  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Oracle zeroes in on Hadoop data with analytics tool by analyticsweekpick

>> 7 Lessons From Apple To Small Business by v1shal

>> The Business of Data by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Can artificial intelligence help stop religious violence? – BBC News Under  Artificial Intelligence

>>
 How to Leverage True Edge Flexibility and Overcome Operational Challenges – Data Center Frontier (blog) Under  Data Center

>>
 Big Data Analytics in Healthcare Market Global 2018: Sales, Market Size, Market Benefits, Upcoming Developments … – Alter Times Under  Prescriptive Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

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

[ FEATURED READ]

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

image

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

[ TIPS & TRICKS OF THE WEEK]

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:Is it better to design robust or accurate algorithms?
A: A. The ultimate goal is to design systems with good generalization capacity, that is, systems that correctly identify patterns in data instances not seen before
B. The generalization performance of a learning system strongly depends on the complexity of the model assumed
C. If the model is too simple, the system can only capture the actual data regularities in a rough manner. In this case, the system poor generalization properties and is said to suffer from underfitting
D. By contrast, when the model is too complex, the system can identify accidental patterns in the training data that need not be present in the test set. These spurious patterns can be the result of random fluctuations or of measurement errors during the data collection process. In this case, the generalization capacity of the learning system is also poor. The learning system is said to be affected by overfitting
E. Spurious patterns, which are only present by accident in the data, tend to have complex forms. This is the idea behind the principle of Occam’s razor for avoiding overfitting: simpler models are preferred if more complex models do not significantly improve the quality of the description for the observations
Quick response: Occam’s Razor. It depends on the learning task. Choose the right balance
F. Ensemble learning can help balancing bias/variance (several weak learners together = strong learner)
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

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[ QUOTE OF THE WEEK]

War is 90% information. – Napoleon Bonaparte

[ PODCAST OF THE WEEK]

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

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

Subscribe 

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[ 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

Nov 01, 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]

>> Adoption of Analytics in Business Increasing but ROI Remains Elusive [INFOGRAPHIC] by bobehayes

>> Perfecting Sensor Data Analytics with Cyberforaging by jelaniharper

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

Wanna write? Click Here

[ NEWS BYTES]

>>
 Second year of ARI Mentor Program builds on success of inaugural challenge – Australasian Leisure Management (press release) Under  Sales Analytics

>>
 Sonja Quale | Confidio – Maryland Daily Record Under  Sales Analytics

>>
 CWH investigates augmented reality, Internet of Things – Australian Journal of Pharmacy (blog) Under  Internet Of Things

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]

On Intelligence

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

[ TIPS & TRICKS OF THE WEEK]

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 likely differences between administrative datasets and datasets gathered from experimental studies. What are likely problems encountered with administrative data? How do experimental methods help alleviate these problems? What problem do they bring?
A: Advantages:
– Cost
– Large coverage of population
– Captures individuals who may not respond to surveys
– Regularly updated, allow consistent time-series to be built-up

Disadvantages:
– Restricted to data collected for administrative purposes (limited to administrative definitions. For instance: incomes of a married couple, not individuals, which can be more useful)
– Lack of researcher control over content
– Missing or erroneous entries
– Quality issues (addresses may not be updated or a postal code is provided only)
– Data privacy issues
– Underdeveloped theories and methods (sampling methods…)

Source

[ VIDEO OF THE WEEK]

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

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

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]

Scott Harrison (@SRHarrisonJD) on leading the learning organization #JobsOfFuture #Podcast

 Scott Harrison (@SRHarrisonJD) on leading the learning organization #JobsOfFuture #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

Oct 25, 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]

>> Betting the Enterprise on Data with Cloud-Based Disaster Recovery and Backups by jelaniharper

>> Free Comparison of 5 Leading Product Analytics Platforms by analyticsweek

>> What does the 5-point/star mobile app rating tell us about user loyalty? by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 Global Digital Media Market report forecasts revenue growth at the global, regional, and country levels and provides … – County Telegram Under  Sales Analytics

>>
 House Passes Slew Of Homeland, Cyber Security Bills – Defense Daily Network Under  cyber security

>>
 Supply Chain Leaders Say Top Priority is Responding to Customers Faster – Material Handling & Logistics Under  Prescriptive Analytics

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]

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:You are compiling a report for user content uploaded every month and notice a spike in uploads in October. In particular, a spike in picture uploads. What might you think is the cause of this, and how would you test it?
A: * Halloween pictures?
* Look at uploads in countries that don’t observe Halloween as a sort of counter-factual analysis
* Compare uploads mean in October and uploads means with September: hypothesis testing

Source

[ VIDEO OF THE WEEK]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. – Atul Butte, Stanford

[ PODCAST OF THE WEEK]

@chrisbishop on futurist's lens on #JobsOfFuture #FutureofWork #JobsOfFuture #Podcast

 @chrisbishop on futurist’s lens on #JobsOfFuture #FutureofWork #JobsOfFuture #Podcast

Subscribe 

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[ FACT OF THE WEEK]

Every second we create new data. For example, we perform 40,000 search queries every second (on Google alone), which makes it 3.5 searches per day and 1.2 trillion searches per year.In Aug 2015, over 1 billion people used Facebook FB +0.54% in a single day.

Sourced from: Analytics.CLUB #WEB Newsletter

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

[  COVER OF THE WEEK ]

image
Conditional Risk  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Four levels of Hadoop adoption maturity by analyticsweekpick

>> Data Management Rules for Analytics by analyticsweek

>> Healthcare Analytics Tips for Business Minded Doctors by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Hitachi Capital partners with Jaywing to improve application credit scores through AI – Finextra Under  Risk Analytics

>>
 Risk Analytics Market Growth Forecast Analysis by Manufacturers, Regions, Type and Application to 2023 – Financial Counselor Under  Risk Analytics

>>
 Commerzbank creates Hadoop-based platform for business-critical insights – ComputerWeekly.com Under  Hadoop

More NEWS ? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

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

[ FEATURED READ]

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]

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

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

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

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

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

Source

[ VIDEO OF THE WEEK]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ 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]

100 terabytes of data uploaded daily to Facebook.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 11, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Trust the data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> 3 Emerging Big Data Careers in an IoT-Focused World by kmartin

>> Customer Loyalty Resource for Customer Experience Professionals by bobehayes

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

Wanna write? Click Here

[ NEWS BYTES]

>>
 Latest technology report on big data security market report explored in latest research – WhaTech Under  Big Data Security

>>
 Snapchat Will Let Media Partners Aggregate, Monetize User Posts – Variety Under  Social Analytics

>>
 How to become a machine learning and AI specialist – Android … – Android Authority (blog) Under  Machine Learning

More NEWS ? Click Here

[ FEATURED COURSE]

Master Statistics with R

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In this Specialization, you will learn to analyze and visualize data in R and created reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform fre… more

[ FEATURED READ]

The Industries of the Future

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The New York Times bestseller, from leading innovation expert Alec Ross, a “fascinating vision” (Forbes) of what’s next for the world and how to navigate the changes the future will bring…. 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 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]

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]

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]

@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]

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

Oct 04, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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statistical anomaly  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> How the lack of the right data affects the promise of big data in India by analyticsweekpick

>> DR. DMITRI WILLIAMS: GAMING ANALYTICS: HOW TO GET THE MOST OUT OF YOUR DATA by analyticsweekpick

>> Apr 12, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

Wanna write? Click Here

[ NEWS BYTES]

>>
 Privacy: Facebook suspends data analytics firm Crimson Hexagon – BetaNews Under  Social Analytics

>>
 Catasys Inc. of Brentwood Reports $4 Million Q2 Loss – Los Angeles Business Journal Under  Health Analytics

>>
 As Nvidia expands in artificial intelligence, Intel defends turf – Reuters Under  Artificial Intelligence

More NEWS ? Click Here

[ FEATURED COURSE]

Probability & Statistics

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This course introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and… more

[ FEATURED READ]

Superintelligence: Paths, Dangers, Strategies

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The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but … more

[ TIPS & TRICKS OF THE WEEK]

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 resampling methods are and why they are useful?
A: * repeatedly drawing samples from a training set and refitting a model of interest on each sample in order to obtain additional information about the fitted model
* example: repeatedly draw different samples from training data, fit a linear regression to each new sample, and then examine the extent to which the resulting fit differ
* most common are: cross-validation and the bootstrap
* cross-validation: random sampling with no replacement
* bootstrap: random sampling with replacement
* cross-validation: evaluating model performance, model selection (select the appropriate level of flexibility)
* bootstrap: mostly used to quantify the uncertainty associated with a given estimator or statistical learning method

Source

[ VIDEO OF THE WEEK]

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

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

Subscribe to  Youtube

[ 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]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Distributed computing (performing computing tasks using a network of computers in the cloud) is very real. Google GOOGL -0.53% uses it every day to involve about 1,000 computers in answering a single search query, which takes no more than 0.2 seconds to complete.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 27, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Conditional Risk  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Why Entrepreneurship Should Be Compulsory In Schools by v1shal

>> Announcing RStudio and Databricks Integration by analyticsweek

>> Why bottom-up innovation is better than top-down innovation? by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 Artificial intelligence has learned to probe the minds of other computers – Science Magazine Under  Artificial Intelligence

>>
 Accenture Acquires Big Data Analytics, AI Consulting Firm Kogentix – ChannelE2E Under  Analytics

>>
 Social and behavioral analytics experts speak at Northwestern – Northwestern University NewsCenter Under  Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Intro to Machine Learning

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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]

Superintelligence: Paths, Dangers, Strategies

image

The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but … more

[ TIPS & TRICKS OF THE WEEK]

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:What is A/B testing?
A: * Two-sample hypothesis testing
* Randomized experiments with two variants: A and B
* A: control; B: variation
* User-experience design: identify changes to web pages that increase clicks on a banner
* Current website: control; NULL hypothesis
* New version: variation; alternative hypothesis

Source

[ VIDEO OF THE WEEK]

Rethinking classical approaches to analysis and predictive modeling

 Rethinking classical approaches to analysis and predictive modeling

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]

@SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

 @SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

Subscribe 

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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

Sep 20, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Human resource  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Map of US Hospitals and their Process of Care Metrics by bobehayes

>> What Is A Creative Data Scientist Worth? by analyticsweekpick

>> Creative Destruction and Risk Taking by ehenry

Wanna write? Click Here

[ FEATURED COURSE]

Python for Beginners with Examples

image

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

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

image

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]

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:Which kernels do you know? How to choose a kernel?
A: * Gaussian kernel
* Linear kernel
* Polynomial kernel
* Laplace kernel
* Esoteric kernels: string kernels, chi-square kernels
* If number of features is large (relative to number of observations): SVM with linear kernel ; e.g. text classification with lots of words, small training example
* If number of features is small, number of observations is intermediate: Gaussian kernel
* If number of features is small, number of observations is small: linear kernel

Source

[ VIDEO OF THE WEEK]

@RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

 @RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

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[ 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]

@TimothyChou on World of #IOT & Its #Future Part 2 #FutureOfData #Podcast

 @TimothyChou on World of #IOT & Its #Future Part 2 #FutureOfData #Podcast

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[ FACT OF THE WEEK]

Every person in the US tweeting three tweets per minute for 26,976 years.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 13, 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

[ AnalyticsWeek BYTES]

>> June 12, 2017 Health and Biotech analytics news roundup by pstein

>> 20 Best Practices for Customer Feedback Programs: Strategy and Governance by bobehayes

>> Anita Sarkeesian’s brave attempt to restore women equality in gaming by d3eksha

Wanna write? Click Here

[ NEWS BYTES]

>>
 New Research Report on Big Data Security Market, 2017-2027 – Latest Market Reports By Abhishek Budholiya (press release) (blog) Under  Big Data Security

>>
 Software-defined networking is turning concern about security in the cloud on its head – Help Net Security Under  Cloud Security

>>
 How Big Data Science and Analytics is the Lure for Businesses Today – Entrepreneur Under  Big Data 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]

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

image

“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]

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 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 with Jon Gibs(@jonathangibs) @L2_Digital

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

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[ 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]

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

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

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[ FACT OF THE WEEK]

In late 2011, IDC Digital Universe published a report indicating that some 1.8 zettabytes of data will be created that year.

Sourced from: Analytics.CLUB #WEB Newsletter