Uncategorized – AnalyticsCube

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

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[ COVER OF THE WEEK ]

Insights Source

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q: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

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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]

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

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[ 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

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Geeks Vs Nerds [Infographics] by v1shal

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

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

Wanna write? Click Here

[ FEATURED COURSE]

Machine Learning

6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending … more

[ FEATURED READ]

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

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]

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: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 dont 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]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @ScottZoldi, @FICO

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[ QUOTE OF THE WEEK]

Data really powers everything that we do. Jeff Weiner

[ PODCAST OF THE WEEK]

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

iTunes GooglePlay

[ FACT OF THE WEEK]

In 2008, Google was processing 20,000 terabytes of data (20 petabytes) a day.

Sourced from: Analytics.CLUB #WEB Newsletter

Jun 06, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Statistics Source

[ LOCAL EVENTS & SESSIONS]

Jun 20, 2019 #WEB Free Introduction to Artificial Intelligence Seminar – Oakland CA
Jun 24, 2019 #WEB Lean Six Sigma Black Belt (LSSBB) Certification Training in Fort Pierce, FL
Jun 09, 2019 #WEB Developing Machine Learning Model Using Google Cloud Platform (Online Webinar)

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Choosing an Analytics Development Approach: How to Optimize Your Business Benefits by analyticsweek

>> Voices in AI â Episode 87: A Conversation with Sameer Maskey by analyticsweekpick

>> PHP Exceeds the Generic Human Expectations. Hereâs how the Brand got it Done by thomassujain

Wanna write? Click Here

[ FEATURED COURSE]

R, ggplot, and Simple Linear Regression

Begin to use R and ggplot while learning the basics of linear regression… more

[ FEATURED READ]

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

Ray Kurzweil is arguably today’s most influentialand often controversialfuturist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilizationreverse… more

[ TIPS & TRICKS OF THE WEEK]

[ 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 IDs). Satellite tables map IDs 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]

Surviving Internet of Things

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[ 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]

Understanding #FutureOfData in #Health & #Medicine – @thedataguru / @InovaHealth #FutureOfData #Podcast

iTunes GooglePlay

[ FACT OF THE WEEK]

In 2015, a staggering 1 trillion photos will be taken and billions of them will be shared online. By 2017, nearly 80% of photos will be taken on smart phones.

Sourced from: Analytics.CLUB #WEB Newsletter

May 30, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Weak data Source

[ LOCAL EVENTS & SESSIONS]

Jun 11, 2019 #WEB Webinar – HR Analytics made easy with Tableau
Jun 06, 2019 #WEB Data Engineering & Analytics Engineering -Python, DB and App.Devs-Global Webinar
Jun 11, 2019 #WEB Intro to Git & GitHub: Your Coding Safety Net

More WEB events? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

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

[ FEATURED READ]

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]

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 an outlier? Explain how you might screen for outliers and what would you do if you found them in your dataset. Also, explain what an inlier is and how you might screen for them and what would you do if you found them in your dataset
A: Outliers:
– An observation point that is distant from other observations
– Can occur by chance in any distribution
– Often, they indicate measurement error or a heavy-tailed distribution
– Measurement error: discard them or use robust statistics
– Heavy-tailed distribution: high skewness, cant use tools assuming a normal distribution
– Three-sigma rules (normally distributed data): 1 in 22 observations will differ by twice the standard deviation from the mean
– Three-sigma rules: 1 in 370 observations will differ by three times the standard deviation from the mean

Three-sigma rules example: in a sample of 1000 observations, the presence of up to 5 observations deviating from the mean by more than three times the standard deviation is within the range of what can be expected, being less than twice the expected number and hence within 1 standard deviation of the expected number (Poisson distribution).

If the nature of the distribution is known a priori, it is possible to see if the number of outliers deviate significantly from what can be expected. For a given cutoff (samples fall beyond the cutoff with probability p), the number of outliers can be approximated with a Poisson distribution with lambda=pn. Example: if one takes a normal distribution with a cutoff 3 standard deviations from the mean, p=0.3% and thus we can approximate the number of samples whose deviation exceed 3 sigmas by a Poisson with lambda=3

Identifying outliers:
– No rigid mathematical method
– Subjective exercise: be careful
– Boxplots
– QQ plots (sample quantiles Vs theoretical quantiles)

Handling outliers:
– Depends on the cause
– Retention: when the underlying model is confidently known
– Regression problems: only exclude points which exhibit a large degree of influence on the estimated coefficients (Cooks distance)

Inlier:
– Observation lying within the general distribution of other observed values
– Doesnt perturb the results but are non-conforming and unusual
– Simple example: observation recorded in the wrong unit (°F instead of °C)

Identifying inliers:
– Mahalanobis distance
– Used to calculate the distance between two random vectors
– Difference with Euclidean distance: accounts for correlations
– Discard them

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Big Data Analytics

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[ QUOTE OF THE WEEK]

Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom. Clifford Stoll

[ PODCAST OF THE WEEK]

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

iTunes GooglePlay

[ FACT OF THE WEEK]

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

Sourced from: Analytics.CLUB #WEB Newsletter