Sep 17, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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SQL Database  Source

[ AnalyticsWeek BYTES]

>> Speed and Trust with Azure Synapse Analytics by analyticsweekpick

>> Mastering Deep Learning with Self-Service Data Science for Business Users by jelaniharper

>> UX Says, UI Says by analyticsweekpick

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[ 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]

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

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If you are looking for a book to help you understand how the machine learning algorithms “Random Forest” and “Decision Trees” work behind the scenes, then this is a good book for you. Those two algorithms are commonly u… 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 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, can’t 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 (Cook’s distance)

Inlier:
– Observation lying within the general distribution of other observed values
– Doesn’t perturb the results but are non-conforming and unusual
– Simple example: observation recorded in the wrong unit (°F instead of °C)

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

Source

[ VIDEO OF THE WEEK]

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

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

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[ QUOTE OF THE WEEK]

The world is one big data problem. – Andrew McAfee

[ PODCAST OF THE WEEK]

Unconference Panel Discussion: #Workforce #Analytics Leadership Panel

 Unconference Panel Discussion: #Workforce #Analytics Leadership Panel

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[ FACT OF THE WEEK]

IDC Estimates that by 2020,business transactions on the internet- business-to-business and business-to-consumer – will reach 450 billion per day.

Sourced from: Analytics.CLUB #WEB Newsletter

The Qualcomm FTC Case Goes To Appeal: The Reasons Qualcomm Should Prevail

I’ve covered a lot of trials over the years, and it has been rare that I’ve seen a US regulatory agency misbehave as much as the FTC (Federal Trade Commission) has in this case. It started as a result of Apple allegedly fabricating evidence and then presenting to a reduced panel of outgoing commissioners during […]

The post The Qualcomm FTC Case Goes To Appeal: The Reasons Qualcomm Should Prevail appeared first on TechSpective.

Source: The Qualcomm FTC Case Goes To Appeal: The Reasons Qualcomm Should Prevail by administrator

White paper: Data integrity in an uncertain world – how to achieve operational resilience

Any successful business must show a level of operational resilience in challenging times.

Whether obstacles arise from internal factors such as expansion and restructuring, or external factors such as increased trading pressures, firms must be able to adapt and thrive operationally.

Is your operations function effective?

This paper explores how data integrity is of paramount importance to successful, resilient firms and examines how automating your data management processes can help reduce costs and eliminate the potential for human error.

Fill in your details to download your free copy:




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Sep 10, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Conditional Risk  Source

[ AnalyticsWeek BYTES]

>> How Manual vs. Automated Occupancy Counting Makes Or Breaks Your Business by analyticsweekpick

>> From Crisis to Competitive Advantage with Data Catalog Collaboration by analyticsweekpick

>> March 20, 2017 Health and Biotech analytics news roundup by pstein

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[ 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]

Introduction to Graph Theory (Dover Books on Mathematics)

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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]

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

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

Source

[ VIDEO OF THE WEEK]

#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]

Data matures like wine, applications like fish. – James Governor

[ PODCAST 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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[ FACT OF THE WEEK]

A quarter of decision-makers surveyed predict that data volumes in their companies will rise by more than 60 per cent by the end of 2014, with the average of all respondents anticipating a growth of no less than 42 per cent.

Sourced from: Analytics.CLUB #WEB Newsletter

Conversation on racism and robotics

Talking about racism and it’s impact on robotics and roboticists was the first conversation in our new biweekly online discussion series “Society, Robots and Us” on alternate Tuesdays at 6pm PDT. It was a generous, honest and painful discussion that I hope has left a lasting impact on everyone who listened. There is systemic racism in America, and this does have an impact on robotics and roboticists in many many ways.

The US Senator Elizabeth Warren in conversation today with Alicia Garza from Black Futures Lab said, “America was founded on principles of liberty and freedom, but it was built on the backs of enslaved people. This is a truth we must not ignore. Racism and white supremacy have shaped every crucial aspect of our economy, and our political system for generations now.”

The speakers in ‘Society, Robots and Us’ were Chad Jenkins, Monroe Kennedy III, Jasmine Lawrence, Tom Williams, Ken Goldberg and Maynard Holliday explored the impact of racism in their experiences in robotics, along with explicit information about changes that we all can make. And we discussed learnings for allies and supporters and what a difference support could make. Please listen to the full discussion but Chad Jenkin’s notes capture some of the critical insights.

[youtube https://www.youtube.com/watch?v=_PHMUJdPs_o?feature=oembed&w=500&h=281]

“I have been in computing for nearly 30 years and a roboticist for over 20 years.  Thus, I have been able to experience firsthand many of the systemic problems that face our field. Let me build on some of the recommendations from the blackincomputing.org open letter and call to action. “

In particular, I believe we can bring equal opportunity to STEM quickly by upholding Title VI of the Civil Rights Act of 1964 and Title IX of the Educational Amendments of 1972 for institutions receiving federal funding, and public funding more generally.  We now incentivize systemic disparate impacts in STEM.

Like law enforcement, university faculty are asked to do too much. Given our bandwidth limits, we have to make hard choices about what gets our attention and effort.

This creates a dilemma in every faculty member about whether to bolster their own personal advancement (by gaining social acceptance in the establishments of the field that control access to funding, hiring, and publishing through peer review) or further create and extend opportunity to others (taking a professional sacrifice to provide mentorship and empathy to future generations towards broadening participation in the STEM workforce).

It is clear STEM incentivizes the former given systemic exclusion of underrepresented minorities, with disastrous results thus far.

I believe we are a vastly better society with the upholding of Title VII of the Civil Rights Act of 1964 yesterday by the Supreme Court to prohibit employment discrimination against LGBTQ+ citizens.  Discrimination is wrong.  My hope is that we can apply this same standard and attention for Title VI of this statue to outcomes in STEM. This is not an issue of altruism, it reflects our true values at a nation and affects the quality of our work and its impact on the world.

There are placeholder measures that can be enacted to incentivize equal opportunity.  For example, universities could decline sabbatical and leave requests from faculty seeking to collaborate with companies that have failed to provide equal opportunity, such as OpenAI and Google DeepMind.

To achieve systemic fairness in robotics, however, we must go beyond token gestures to address the causal factors of inequity rooted in the core economic incentives of our universities.  It is universities that are the central ladder to opportunity through the development of future leaders, innovators, and contributors to our society.

We have the tools at hand today to create equal opportunity in STEM.  The question is whether we have the will.

Equal opportunity cannot be true for anyone unless equal opportunity is true for everyone.

Odeste Chadwicke Jenkins, Associate Professor University of Michigan Robotics Institute

Our next episode of “Society, Robots and Us” on June 30 is going to discuss the role and the roll out of killer robots, but we’ll be coming back to talk more about racism, diversity and inclusion in robotics because we’ve only just scratched the surface.

Source by analyticsweekpick

Chinese regulators consider AliPay and WeChat antitrust probe

The Chinese central bank is considering the launch of an antitrust probe into the conduct of digital payments giants Alipay and WeChat Pay.

AliPay sign

Alipay and WeChat have a firm grip on the Chinese payments market

According to Reuters sources, the State Council has gathered evidence on the two firms for more than a month.

It plans to investigate whether the payments giants have used their dominant market position to prevent competition from gaining a significant foothold in China.

Both Alipay, owned by Ant Group and Alibaba, and Tencent Holding’s WeChat Pay are lobbying government officials to prevent the probe.

The investigation would be ill-timed for Ant Group, as it pursues a dual listing in Hong Kong and Shanghai. The firm is seeking a $200 billion valuation.

Reuters sources report that the People’s Bank of China (PBOC) is taking the suggestion of an investigation “very seriously.”

Saturation

AliPay and WeChat enable payment through the scanning of a QR code. The two payments systems dominate a Chinese payment sector valued at more than $16 trillion.

Alipay has 520 million active users, while competitor WeChat Pay claims more than 800 million. The latter also reports more than one billion commercial transactions per day.

Tencent’s mobile payment systems have a market penetration of 84%, with AliPay marginally behind with a 63.6% penetration, according to Ipsos data.

The closest competitor to either is China Unionpay’s Quick Pass solution, with 11.6%.

Chinese authorities have planned to reign in the dominance of the payment giants for some time. In April 2018 the PBOC announced intentions to standardise and regulate QR code payments.

Related: Mastercard extends cross-border services in China with Bank of Shanghai

Source

Sep 03, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Accuracy check  Source

[ AnalyticsWeek BYTES]

>> The Four Horsemen of SASE (Secure Access Service Edge) by administrator

>> R now supported in Azure SQL Database by analyticsweekpick

>> Social Media Analytics – What to Measure for Success? by thomassujain

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[ FEATURED COURSE]

Artificial Intelligence

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

[ FEATURED READ]

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]

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:How would you define and measure the predictive power of a metric?
A: * Predictive power of a metric: the accuracy of a metric’s success at predicting the empirical
* They are all domain specific
* Example: in field like manufacturing, failure rates of tools are easily observable. A metric can be trained and the success can be easily measured as the deviation over time from the observed
* In information security: if the metric says that an attack is coming and one should do X. Did the recommendation stop the attack or the attack never happened?

Source

[ VIDEO OF THE WEEK]

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

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

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[ QUOTE OF THE WEEK]

He uses statistics as a drunken man uses lamp posts—for support rather than for illumination. – Andrew Lang

[ 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]

Data production will be 44 times greater in 2020 than it was in 2009.

Sourced from: Analytics.CLUB #WEB Newsletter

Numpy Tutorial for Beginners

What is NumPy? Numpy is a tool for mathematical computing and data preparation in Python. It can be utilized to perform a number of mathematical operations on arrays such as trigonometric, statistical and algebraic routines. This library provides many useful features including handling n-dimensional arrays, broadcasting, performing operations, data generation, etc., thus, it’s the fundamental […]

The post Numpy Tutorial for Beginners appeared first on GreatLearning.

Source by administrator