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

[  COVER OF THE WEEK ]

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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

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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]

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

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[ 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

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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

#Compliance and #Privacy in #Health #Informatics by @BesaBauta

#Compliance and #Privacy in #Health #Informatics by @BesaBauta

In this podcast @BesaBauta from MeryFirst talks about the compliance and privacy challenges faced in hyper regulated industry. With her experience in health informatics, Besa shared some best practices and challenges that are faced by data science groups in health informatics and other similar groups in regulated space. This podcast is great for anyone looking to learn about data science compliance and privacy challenges.

Besa’s Recommended Read:
The Art Of War by Sun Tzu and Lionel Giles https://amzn.to/2Jx2PYm

Podcast Link:
iTunes: http://math.im/itunes
GooglePlay: http://math.im/gplay

Besa’s BIO:
Dr. Besa Bauta is the Chief Data Officer and Chief Compliance Officer for MercyFirst, a social service organization providing health and mental health services to children and adolescents in New York City. She oversees the Research, Evaluation, Analytics, and Compliance for Health (REACH) division, including data governance and security measures, analytics, risk mitigation, and policy initiatives.
She is also an Adjunct Assistant Professor at NYU, and previously worked as a Research Director for a USAID project in Afghanistan, and as the Senior Director of Research and Evaluation at the Center for Evidence-Based Implementation and Research (CEBIR). She holds a Ph.D. in implementation science with a focus on health services, an MPH in Global Health and an MSW. Her research has focused on health systems, mental health, and integration of technology to improve population-level outcomes.

About #Podcast:
#FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Want to sponsor?
Email us @ info@analyticsweek.com

Keywords:
#FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Source: #Compliance and #Privacy in #Health #Informatics by @BesaBauta by v1shal

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

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

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

Subscribe to  Youtube

[ 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

CMS Predictive Readmission Models ‘Not Very Good’

Researchers find that functional status, rather than comorbidity, is a better predictor of whether someone will be readmitted to the hospital.

The way the Centers for Medicare & Medicaid Services predicts readmissions is likely neither the most accurate nor the fairest, researchers at Harvard Medical School claim.

A study published in the May issue of the Journal of General Internal Medicine found that functional status, rather than comorbidities, was a better predictor of whether someone would be readmitted to the hospital.

“This raises a question of whether Medicare is really using the best predictors to really understand readmission,” as well as questions about how fairly hospitals are being financially penalized, says principal investigator Jeffrey Schneider, MD, medical director of the Trauma, Burn and Orthopedic Program at Spaulding Rehabilitation Hospital in Boston and assistant professor of physical medicine and rehabilitation at Harvard Medical School.

Trotter

Jeffrey Schneider, MD

Schneider points out that CMS fined more than 2,200 hospitals a total of $280 million in 2013 for excess 30-day hospital readmissions, so having accurate readmission models is critical.

But the ones CMS uses “are not very good predictive models, and they have relied heavily on simple demographic data like age and gender and comorbidities,” he says.

Moreover, “there’s mounting evidence that function is a good predictor of all sorts of hospital outcomes.”

The researchers conducted a retrospective study of 120,957 patients in the Uniform Data System for Medical Rehabilitation database who were admitted to inpatient rehabilitation facilities under the medically complex impairment group code between 2002 and 2011.

Schneider says they chose to study this “medical complex” population “because it is heterogeneous and we think well-represents a wide swath of patients who are in a hospital for medical reasons.”

“Rehabilitation hospitals routinely collect functional measures and that data is available in a large administrative database,” he says. The researchers measured functional status using the Functional Independence Measure (FIM), which looks at 18 tasks such as eating, dressing, bathing, toileting, grooming, and climbing stairs. Each of the 18 items is rated on a seven-point scale from completely dependent on someone else for help to totally independent.

FIM data is collected on a patient’s admission to a rehab facility—which is usually on the same day as their discharge from an acute care facility. “In that way it’s also a surrogate marker of their functional status when they left acute care,” he says.

Function or Comorbidity?

Researchers built models based on functional status and gender to predict readmission at three, seven, and 30 days, and compared them to three different models based on comorbidities and gender.

“We really just wanted to answer this question: If function was a better measure of readmission than comorbidity,” Schneider says. “We didn’t seek to build the best model.”

The researchers then determined the c-statistic—the measure of a model’s overall ability to predict an outcome, which ranges from 0.5 (chance) to 1 (perfect predictor)—of the models.

They found that the model with gender and function was significantly better at predicting readmissions, Schneider says.

Models based on function and gender for three-, seven-, and 30-day readmissions (c-statistics 0.691, 0.637, and 0.649, respectively) performed significantly better than even the best-performing model based on comorbidities and gender (c-statistics 0.572, 0.570, and 0.573, respectively).

Even adding comorbidities to the function-based models didn’t help much, creating c-statistic differences of only 0.013, 0.017, and 0.015 for 3-, 7-, and 30-day readmissions, respectively, for the best-performing model.

‘It’s So Intuitive’

Why is function a good predictor? Schneider says it may represent something else, such as the severity of a patient’s illness. Cancer patients, for instance, have a wide degree of functional statuses depending on how sick they are. In this way, “it’s so intuitive” that function would be a good predictor of readmissions, he says. If you can’t care for yourself, you’ll likely end up back in the hospital.

In addition, “comorbidity is a fixed variable,” Schneider says, but function is not. And since function is a better predictor of readmission, even at shorter time intervals, assessing a patient’s functional status and doing things to improve it could be a way of reducing preventable readmissions, especially the three- and seven-day readmissions.

“Acute care hospitals are not routinely collecting a functional measure of their patients,” Schneider says. He also points out that recent research on functional interventions—such as early mobilization in the ICU—in acute care hospitals is showing to improve patient outcomes.

Next Steps

“I think the next wave for hospitals… is [thinking about] how to make use of this information,” Schneider says, by piloting functional interventions and determining functional measures at discharge to help with risk-stratifying for readmissions.

On a larger scale, there’s also the policy perspective that CMS’s readmissions models aren’t as good as they could be. Schneider says he and his colleagues are conducting another, even larger study, using the same framework, but looking at but all patients in a rehab hospital, not only at medically complex ones. He says it hasn’t been published yet, but the findings will be pretty similar.

“I think it’s really worthwhile,” he says.

 

To read the original article on HealthLeaders Media, click here.

Source

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

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“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

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]

@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

Wrapping my head around Big-data problem

Last week at a meetup in Boston, I was told to give my 2 cents on big-data with an analogy. Idea is to make the problem understandable even by a 12 year old, and that made me think. So, based on what all I have gathered, and seen from my experience, what exactly is big-data and is there a simple analogy to explain it to people.

Here is my 2 cents: wait for it.. Wait for it.. “Your Big-data problem is like your garbage problem”.

Garbage:

Things that we don’t know what to do with or how to use.

Things that we have not used for ages.

Things that we have used enough and found it is of no further use.

Someone else’s garbage that might have something that is of some use to you.

Big data includes:

Data sets that we capture but are not sure what to do or how to use.

Data-sets sitting out there that has not been monitored or used for ages.

Data-sets that comprise of information that we think are sufficient for helping us make business decisions.

And Data-sets captured by others that might be of some strategic relevance to us.

I have been talking with a couple of fortune 100 organization’s big-data team members and asked about their big-data initiatives. Findings were clear – that is, big-data is not clear enough. Let me try to explain what is going on:

Say, you have tons of garbage that you are concerned about and you want to make sure nothing useful is thrown out. Now, you are handed a shiny glove(tools) to help you help yourself by digging through your data. Is this picture looking right? This is what most of the companies are struggling with. Sure, they can deal with their garbage but it’s not their core competency. Your core job is not to filter through that garbage. This puts you at high risk of failing.

Very few smart companies are doing it right by calling experts to look at their big-data and help them with cleansing. This helps them do it more efficiently. You save on trial-error cost; you get to best practices first and adopt it in your core sooner.

So, it is important for companies to realize who best can serve as their Waste Management professionals. It won’t even hurt if a redundancy is infused to help get to best solutions faster and minimize failure risk.

Therefore, garbage is the best analogy I have found to explain big-data problem and how to resolve it. Surely, I am all ears to listen to better analogy that simplifies the meaning and sheds appropriate light into this issue.

Stay tuned, I will be posting a playbook for helping companies get started with resolving their big-data problem faster and cheaper.

 

Source by v1shal

Types of AI: From Reactive to Self-Aware [Infographics]

Artificial intelligence (AI) – intelligence exhibited by machines or software. It is also the name of the academic field which studies how to create computers and computer software that are capable of intelligent behaviour. There is an interesting infographics talking about types of AI that are available: Reaching, Limited Memory, Theory of Mind and Self-Aware.

source: Futurism

Source

Sep 06, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Complex data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> On Ingesting Kate Crawford’s “The Trouble with Bias” by analyticsweek

>> Customer Loyalty 2.0 Article in Quirk’s Marketing Research Review by bobehayes

>> Traditional Retailers, It’s 15 Minutes to midnight. How to Strike Back at Your Online Competitors. #Retail Transformation, #Retail Analytics by tony

Wanna write? Click Here

[ NEWS BYTES]

>>
 Mass Incarceration’s Complex Statistics – CityLab Under  Statistics

>>
 Most hospitals won’t reach HIMSS Analytics Stage 7 until 2035, study finds – Becker’s Hospital Review Under  Analytics

>>
 Elder abuse statistics are state’s worst for the ‘quiet epidemic’ – Staunton News Leader Under  Statistics

More NEWS ? Click Here

[ FEATURED COURSE]

CPSC 540 Machine Learning

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Machine learning (ML) is one of the fastest growing areas of science. It is largely responsible for the rise of giant data companies such as Google, and it has been central to the development of lucrative products, such … 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]

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:How do you take millions of users with 100’s transactions each, amongst 10k’s of products and group the users together in meaningful segments?
A: 1. Some exploratory data analysis (get a first insight)

* Transactions by date
* Count of customers Vs number of items bought
* Total items Vs total basket per customer
* Total items Vs total basket per area

2.Create new features (per customer):

Counts:

* Total baskets (unique days)
* Total items
* Total spent
* Unique product id

Distributions:

* Items per basket
* Spent per basket
* Product id per basket
* Duration between visits
* Product preferences: proportion of items per product cat per basket

3. Too many features, dimension-reduction? PCA?

4. Clustering:

* PCA

5. Interpreting model fit
* View the clustering by principal component axis pairs PC1 Vs PC2, PC2 Vs PC1.
* Interpret each principal component regarding the linear combination it’s obtained from; example: PC1=spendy axis (proportion of baskets containing spendy items, raw counts of items and visits)

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Big Data is not the new oil. – Jer Thorp

[ PODCAST OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

 Understanding Data Analytics in Information Security with @JayJarome, @BitSight

Subscribe 

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[ FACT OF THE WEEK]

According to execs, the influx of data is putting a strain on IT infrastructure. 55 percent of respondents reporting a slowdown of IT systems and 47 percent citing data security problems, according to a global survey from Avanade.

Sourced from: Analytics.CLUB #WEB Newsletter