Nov 15, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Statistics  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ NEWS BYTES]

>>
 Why Big Data and Machine Learning are Essential for Cyber Security – insideBIGDATA Under  Big Data Analytics

>>
 Amazon’s Big Step Into IoT – Seeking Alpha Under  IOT

>>
 Hitachi and Tencent team up on ‘internet of things’ – Nikkei Asian Review Under  Internet Of Things

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]

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]

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 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]

@JustinBorgman on Running a data science startup, one decision at a time #Futureofdata #Podcast

 @JustinBorgman on Running a data science startup, one decision at a time #Futureofdata #Podcast

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[ QUOTE OF THE WEEK]

Everybody gets so much information all day long that they lose their common sense. – Gertrude Stein

[ 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]

Brands and organizations on Facebook receive 34,722 Likes every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

To Trust A Bot or Not? Ethical Issues in AI

Given we see fake profiles and potentially chatbots that misfire and miscommunicate we would like your thoughts on whether there should be some sort of Government registry for robots so that consumers know they are legitimate or not. If we had a registry for trolls and or chatbots would that ensure that people could feel more comfortable that they are dealing with a legitimate business or would know if the profile or troll or bot is fake? Is it time for a good housekeeping seal of approval for AI?

These are all provocative questions and questions that are so new I am not sure there is one answer as they are so undefined. What do you think? Who should create such standards? Perhaps we should start by categorizing the types of AI?

Source by tony

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]

>> The Modern Data Warehouse – Enterprise Data Curation for the Artificial Intelligence Future by analyticsweek

>> Analyzing Big Data: A Customer-Centric Approach by bobehayes

>> Analytics Implementation in 12 Steps: An Exhaustive Guide (Tracking Plan Included!) by analyticsweek

Wanna write? Click Here

[ NEWS BYTES]

>>
 Why Cloud (By Default) Gives You Security You Couldn’t Afford Otherwise – Forbes Under  Cloud Security

>>
  Under  Big Data Security

>>
 Make it so: Red River Mill Employees FCU rebrands as Engage – Credit Union Journal Under  Talent Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Baseball Data Wrangling with Vagrant, R, and Retrosheet

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

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:Is it better to spend 5 days developing a 90% accurate solution, or 10 days for 100% accuracy? Depends on the context?
A: * “premature optimization is the root of all evils”
* At the beginning: quick-and-dirty model is better
* Optimization later
Other answer:
– Depends on the context
– Is error acceptable? Fraud detection, quality assurance

Source

[ VIDEO OF THE WEEK]

Surviving Internet of Things

 Surviving Internet of Things

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data beats emotions. – Sean Rad, founder of Ad.ly

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

94% of Hadoop users perform analytics on large volumes of data not possible before; 88% analyze data in greater detail; while 82% can now retain more of their data.

Sourced from: Analytics.CLUB #WEB Newsletter

Three final talent tips: how to hire data scientists

This last post focuses on less tangible aspects, related to curiosity, clarity about what kind of data scientist you need, and having appropriate expectations when you hire.

8. Look for people with curiosity and a desire to solve problems

Radhika Kulkarni, PhD in Operations Research, Cornell University, teaching calculus as a grad student.

 

 

 

 

 

 

 

As I  blogged previously, Greta Roberts of Talent Analytics will tell you that the top traits to look for when hiring analytical talent are curiosity, creativity, and discipline, based on a study her organization did of data scientists. It is important to discover if your candidates have these traits, because they are necessary elements to find a practical solution and separate candidates from those who may get lost in theory. My boss Radhika Kulkarni, the VP of Advanced Analytics R&D at SAS, self-identified this pattern when she arrived at Cornell to pursue a PhD in math. This realization prompted her to switch to operations research, which she felt would allow her to pursue investigating practical solutions to problems, which she preferred to more theoretical research.

That passion continues today, as you can hear Radhika describe in this video on moving the world with advanced analytics. She says “We are not creating algorithms in an ivory tower and throwing it over the fence and expecting that somebody will use it someday. We actually want to build these methods, these new procedures and functionality to solve our customers’ problems.” This kind of practicality is another key trait to evaluate in your job candidates, in order to avoid the pitfall of hires who are obsessed with finding the “perfect” solution. Often, as Voltaire observed, “Perfect is the enemy of good.” Many leaders of analytical teams struggle with data scientists who haven’t yet learned this lesson. Beating a good model to death for that last bit of lift leads to diminishing returns, something few organizations can afford in an ever-more competitive environment. As an executive customer recently commented during the SAS Analytics Customer Advisory Board meeting, there is an “ongoing imperative to speed up that leads to a bias toward action over analysis. 80% is good enough.”

9. Think about what kind of data scientist you need

Ken Sanford, PhD in Economics, University of Kentucky, speaking about how economists make great data scientists at the 2014 National Association of Business Economists Annual Meeting. (Photo courtesy of NABE)

Ken Sanford describes himself as a talking geek, because he likes public speaking. And he’s good at it. But not all data scientists share his passion and talent for communication. This preference may or may not matter, depending on the requirements of the role. As this Harvard Business Review blog post points out, the output of some data scientists will be to other data scientists or to machines. If that is the case, you may not care if the data scientist you hire can speak well or explain technical concepts to business people. In a large organization or one with a deep specialization, you may just need a machine learning geek and not a talking one! But many organizations don’t have that luxury. They need their data scientists to be able to communicate their results to broader audiences. If this latter scenario sounds like your world, then look for someone with at least the interest and aptitude, if not yet fully developed, to explain technical concepts to non-technical audiences. Training and experience can work wonders to polish the skills of someone with the raw talent to communicate, but don’t assume that all your hires must have this skill.

10. Don’t expect your unicorns to grow their horns overnight

Annelies Tjetjep, M.Sc., Mathematical Statistics and Probability from the University of Sydney, eating frozen yogurt.

Annie Tjetjep relates development for data scientists to frozen yogurt, an analogy that illustrates how she shines as a quirky and creative thinker, in addition to working as an analytical consultant for SAS Australia. She regularly encounters customers looking for data scientists who have only chosen the title, without additional definition. She explains: “…potential employers who abide by the standard definitions of what a ‘data scientist’ is (basically equality on all dimensions) usually go into extended recruitment periods and almost always end up somewhat disappointed – whether immediately because they have to compromise on their vision or later on because they find the recruit to not be a good team player….We always talk in dimensions and checklists but has anyone thought of it as a cycle? Everyone enters the cycle at one dimension that they’re innately strongest or trained for and further develop skills of the other dimensions as they progress through the cycle – like frozen yoghurt swirling and building in a cup…. Maybe this story sounds familiar… An educated statistician who picks up the programming then creativity (which I call confidence), which improves modelling, then business that then improves modelling and creativity, then communication that then improves modelling, creativity, business and programming, but then chooses to focus on communication, business, programming and/or modelling – none of which can be done credibly in Analytics without having the other dimensions. The strengths in the dimensions were never equally strong at any given time except when they knew nothing or a bit of everything – neither option being very effective – who would want one layer of froyo? People evolve unequally and it takes time to develop all skills and even once you develop them you may choose not to actively retain all of them.”

So perhaps you hire someone with their first layer of froyo in place and expect them to add layers over time. In other words, don’t expect your data scientists to grow their unicorn horns overnight. You can build a great team if they have time to develop as Annie describes, but it is all about having appropriate expectations from the beginning.

To learn more, check out this series from SAS on data scientists, where you can read Patrick Hall’s post on the importance of keeping the science in data science, interviews with data scientists, and more.

And if you want to check out what a talking geek sounds like, Ken will be speaking at a National Association of Business Economists event next week in Boston – Big Data Analytics at Work: New Tools for Corporate and Industry Economics. He’ll share the stage with another talking geek, Patrick Hall, a SAS unicorn I wrote about it in my first post.

To read the original article on SAS, click here.

Originally Posted at: Three final talent tips: how to hire data scientists by analyticsweekpick

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

Download Free Report from TCELab: Improving the Patient Experience

The Centers for Medicare & Medicaid Services (CMS) will be using patient feedback about their care as part of their reimbursement plan for acute care hospitals (see Hospital Value-Based Purchasing (VBP) program). The purpose of the VBP program is to promote better clinical outcomes for patients and improve their experience of care during hospital stays. Not surprisingly, hospitals are focusing on improving the patient experience (PX) to ensure they receive the maximum of their incentive payments.

Free Download of Research Report on the Patient Experience

I spent the past few months conducting research on and writing about the importance of patient experience (PX) in US hospitals. My partners at TCELab have helped me summarize these studies into a single research report, Improving the Patient Experience . As far as I am aware, these series of studies are the first to integrate these disparate US hospital data sources (e.g., Patient Experience, Health Outcomes, Process of Care, and Medicare spending per patient) to apply predictive analytics for the purpose of identifying the reasons behind a loyal patient base.

While this research is really about the entirety of US hospitals, hospitals still need to dig deeper into their own specific patient experience data to understand what they need to do to improve the patient experience. This report is a good starting point for hospitals to learn what they need to do to improve the patient experience and increase patient loyalty. Read the entire press release about the research report, Improving the Patient Experience.

Get the free report from TCELab by clicking the image or link below:

Download Free Report from TCELab: Improving the Patient Experience

 

 

Originally Posted at: Free Research Report on the State of Patient Experience in US Hospitals by bobehayes

Nov 01, 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]

>> Can Police Use Data Science to Prevent Deadly Encounters? by analyticsweekpick

>> SDN and network function virtualization market worth $ 45.13 billion by 2020 by analyticsweekpick

>> Digital Marketing 2.0 – Rise of the predictive analytics by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 The Rise of Illiberal Artificial Intelligence – National Review Under  Artificial Intelligence

>>
 2018-2023 Prescriptive Analytics Market Overview, Growth, Types, Applications, Market Dynamics, Companies … – Stock Analysis Under  Prescriptive Analytics

>>
 KBI releases Kansas crime statistics for 2017 – WIBW News Now Under  Statistics

More NEWS ? Click Here

[ FEATURED COURSE]

CS229 – Machine Learning

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This course provides a broad introduction to machine learning and statistical pattern recognition. … 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]

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

[ DATA SCIENCE Q&A]

Q:Provide examples of machine-to-machine communications?
A: Telemedicine
– Heart patients wear specialized monitor which gather information regarding heart state
– The collected data is sent to an electronic implanted device which sends back electric shocks to the patient for correcting incorrect rhythms

Product restocking
– Vending machines are capable of messaging the distributor whenever an item is running out of stock

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data at Work: Paul Sonderegger

 @AnalyticsWeek: Big Data at Work: Paul Sonderegger

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

We chose it because we deal with huge amounts of data. Besides, it sounds really cool. – Larry Page

[ 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

Subscribe 

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[ FACT OF THE WEEK]

Decoding the human genome originally took 10 years to process; now it can be achieved in one week.

Sourced from: Analytics.CLUB #WEB Newsletter

Uber: When Big Data Threatens Local Democracy

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Big data is threatening to crush local democracy across the country–and if it succeeds, it may distort local transit and infrastructure development for decades to come.

As Uber has sought to dominate the local taxi industry from Delhi to New York City, the company has deployed its multi-billion dollar venture capital war chest to fight politicians across the country and world, often ignoring local laws as it introduced its app and drivers into the heavily regulated taxi industry.  In New York City, a bill has been introduced to limit the growth of the company locally while the City Council studies the implications for the local taxi industry.

Yesterday, Uber added an attack ad against the City’s mayor Bill De Blasio on the front page of its hailing app, melding its attempt to control local taxi service with seeking control of local politics.  In doing so, it highlights the danger of letting multi-billion dollar global corporations control any part of local transit or other infrastructure, since it gives them a stake in distorting local politics as well.

Uber may be a young company but they have entered old-style politics with a vengeance, hiring David Plouffe, the former strategist for President Obama’s 2008 election campaign to help direct a team of 250 lobbyists operating in at least 50 cities and states around the country.  On top of vast financial resources for traditional lobbying, they control an equally important resource – data and communication with voters throughout local constituencies.  In local political fights, Uber has used email and its app real estate to launch multiple attacks on political opponents.

An Opening Salvo in the Politics of Local Logistics

The fight over Uber is not about who runs local taxis, but really about who will control local transit and related infrastructure in the future.  Uber has made it clear its ambitions go far beyond taxis to encompass what Inc. magazine calls “the future of logistics.”

The data Uber collects on users and local transportation can be converted into delivery services or, as writer Ken Roose explains, “like Amazon, it can become something akin to an all-purpose utility–it’ll just be a way you get things and go places.” Uber has already launched a prototype “Uber Cargo” delivery business in Hong Kong and food delivery and courier services in other cities.

This ties into plans by companies like Google and Tesla to introduce driverless cars and a rush of tech companies to control the logistics and information related to local economies and commerce.   Driverless taxis are the obvious long-term next step for a company like Uber and could reshape urban transportation as fundamentally as the original introduction of the automobile.

The big data companies are already gearing up for the politics of controlling the next generation of urban infrastructure.  Google for example now spends more on lobbying that any other company, a large part of it ($18.2 million) on federal lobbying, but the company has also built a network of state lobbyists to help it on local fights like legalizing its driverless car project.

Driverless cars combined with Uber data – and Google is a major investor in Uber – could remake urban transit, especially as local laws and infrastructure are changed to accommodate them.   Analysts are already discussing how the “‘transportation cloud’…will quickly become dominant form of transportation – displacing far more than just car ownership, it will take the majority of users away from public transportation as well.”

History of Global Corporations Distorting Local Transit Development

With so much at stake, the danger of letting big data money gut local democratic decision-making is obvious.  We only need look to the history of how the auto industry used its political muscle to literally pave the way for destroying local mass transit in multiple cities and pushing highways and suburbanization.  Some of that movement was going to happen naturally, but the auto companies sped the process along and deepened it with actions such as buying up local trolley systems and converting them to bus systems.

City streets, which had been a shared resource of cars, bikes and pedestrians, were converted to car-only use. Where car drivers were once held criminally liable for any pedestrian killed by their car, car companies launched major lobbying campaigns to create a new crime, “jaywalking” that put responsibility on pedestrians not to be in the cars’ way.

Like Uber, the car companies used the communication infrastructure of the day, in that case wire services for reporters, to seize control of the public debate on use of streets.  The National Automobile Chamber of Commerce encouraged reporters to send basic details of traffic accidents to their service and would receive back a complete article to print the next day, with the articles shifting the blame for accidents to pedestrians.

The result of decades of car industry lobbying was the gutting of much of urban America.

Fighting Monopoly as a Political Problem

With momentous political decisions facing local governments as big data, driverless cars and other technologies reshape local transit and logistics, we need to worry about big data monopolies not only distorting local industries at the economic level but also how their political power may distort political decision-making as well.

Uber is backed by an array of economic players, from Google to Goldman Sachs and local politicians should recognize that legalizing Uber is not just adding an additional economic player to the local economy.  It will add a political player willing to spend billions of dollars with the goal of establishing a global logistics behemoth–and seemingly willing to waste any politicians who get in its way.

If Uber is going to misuse its economic power to try to control local political institutions — including real estate on its apps as political attack ads — local governments should feel justified in restricting its growth until both the potential economic and political problems of an emerging taxi monopolist are addressed.  And it raises the broader issue of how big data’s political power needs to be restrained to ensure communities get to decide how best to use technology, rather than the technology companies deciding how best to use communities for their own economic interests.

Note: This article originally appeared in Huffington Post. Click for link here.

Originally Posted at: Uber: When Big Data Threatens Local Democracy by analyticsweekpick