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Three careers that will dominate social media (and it’s not what you think)

Three careers that will dominate social media (and it’s not what you think) | What is Data Science | Scoop.it
The transformation to a social organization will take new skill sets. Marketing? PR? No. Think again.
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What is Data Science
So what the heck is data science any way?
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Data Science is real » Great Lists to Follow

Data Science is real » Great Lists to Follow | What is Data Science | Scoop.it
Carla Gentry CSPO's insight:

There are some great lists out there to follow for Data Science and Technology – Check out a few I have been honored to be listed on – Please feel free to check out everyone on these lists, some really great folks out there!

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Best solution to a problem: data science versus statistical paradigm

Best solution to a problem: data science versus statistical paradigm | What is Data Science | Scoop.it
In the end, there is no such thing as a real data scientist or statistician. It's all about a personal feeling reflecting your career. Some statisticians are more data scientist than me. You can say the same thing about any profession.
Carla Gentry CSPO's insight:

The definition of 'best' depends on which school you follow. Data science and classic statistical science are at the opposite ends of the spectrum. So let's clarify what 'best solution' means in these two opposite contexts:

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Big Data, IOT and Security - OH MY!

Big Data, IOT and Security - OH MY! | What is Data Science | Scoop.it
While we aren’t exactly “following the yellow brick road” these days, you may be feeling a bit like Dorothy from the “Wizard of Oz” when it comes to these topics. No my friend, you aren’t in Kansas anymore!
Carla Gentry CSPO's insight:
Let’s talk about security and data breaches – Now you may be thinking I’m in analytics or data science why should I be concerned with security? Let’s take a look at several breaches that have made the headlines lately.
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Dealing with Supply Chain Big Data

Dealing with Supply Chain Big Data | What is Data Science | Scoop.it
Many supply chain applications based on relational databases will chug along just fine for some time to come.  However, as supply chains get bigger, more complex, and demand faster responses times, expect to see NoSQL databases and complex event processing gaining more than a foothold too.
Carla Gentry CSPO's insight:

With the three V’s stretching conventional data management approaches to the limit, a number of alternatives have emerged – including Hadoop.  Two categories that are likely to find their way into supply chain applications include:

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Make sure big data doesn't land you in big trouble

Make sure big data doesn't land you in big trouble | What is Data Science | Scoop.it
Because data arrives from so many different sources, often in real time, it is also important to validate the input so that malicious actors don't pollute the data set. Reavis's organisation recommends building systems that validate endpoints and detect malicious data feeds.
Carla Gentry CSPO's insight:

Another particular challenge of big data is that it is far more temporally focused than yesterday’s business intelligence systems. These were mostly static affairs, with snapshots of sales and other data updated on a regular, but far from real-time, basis. You could run reports on monthly sales data, for example.

Big data is different because it comes in from multiple sources much more frequently, some being updated in real or near-real time.

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Carla Gentry CSPO's curator insight, December 2, 3:10 PM
The more information you give away about that person, the less entropy you are left with. If you live in metro Vancouver (population 2.3 million), then that drops to 22 bits of entropy. And if someone can start cross-referencing that stuff with information from social media feeds and time series data, the bits will drop off fairly rapidly. Narayanan sliced away enough entropy from Netflix’s anonymised data set of user activity to identify several individual user records.
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Data Science is Dead - Long Live the Data Scientist

Data Science is Dead - Long Live the Data Scientist | What is Data Science | Scoop.it
While there is much hype and confusion around "data science" and the hot new professional "data scientist", there appears to be a growing number of articles and blogs asserting a data scientist bubble and the coming death of data science.
Carla Gentry CSPO's insight:

While there is much hype and confusion around "data science" and the hot new professional "data scientist", there appears to be a growing number of articles and blogs asserting a data scientist bubble and the coming death of data science. #datasciencectrll

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96% of providers say they can’t handle big data, analytics | HealthITAnalytics.com

Social technologies are also popular, the survey found, despite continued skepticism about the value of patient-generated data from wearable tracking devices.  Almost a third of hospitals are collecting wearable device data, while 52% are using social media tactics to communicate with patients, send reminders, and help them keep track of their medications.
Carla Gentry CSPO's insight:

Accurate diagnosis is the first step on the journey to a cure,” said Steve O’Keeffe, founder of MeriTalk. “The healthcare industry needs to change its IT diet to ensure better healthcare outcomes for America.

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Doing Data Science

Doing Data Science | What is Data Science | Scoop.it
There’s a lot of hype about big data talked by people who are just using the latest buzz words, but this book is written by people who are actually working with real live big data.
Carla Gentry CSPO's insight:

There’s a lot of hype about big data talked by people who are just using the latest buzz words, but this book is written by people (like myself) who are actually working with real live big data.

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One Page R: A Survival Guide to Data Science with R

One Page R: A Survival Guide to Data Science with R | What is Data Science | Scoop.it
Many of the documents have been developed and tested whilst visiting the Shenzhen Institutes of Technology as an International Visiting Professor of the Chinese Academy of Sciences. The data used is available for download as data.zip.
Carla Gentry CSPO's insight:

Many of the documents have been developed and tested whilst visiting the Shenzhen Institutes of Technology as an International Visiting Professor of the Chinese Academy of Sciences. The data used is available for download as data.zip.

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Forget GMOs. The Future of Food Is Data--Mountains of It | WIRED

Forget GMOs. The Future of Food Is Data--Mountains of It | WIRED | What is Data Science | Scoop.it
Led by Dan Zigmond—who previously served as chief data scientist for YouTube, then Google Maps—this ambitious project aims to accelerate the work of all the biochemists, food scientists, and chefs on the first floor, providing a computer-generated shortcut to what Hampton Creek sees as the future of food. “We’re looking at the whole process,” Zigmond says of his data team, “trying to figure out what it all means and make better predictions about what is going to happen next.”
Carla Gentry CSPO's insight:

Led by Dan Zigmond—who previously served as chief data scientist for YouTube, then Google Maps—this ambitious project aims to accelerate the work of all the biochemists, food scientists, and chefs on the first floor, providing a computer-generated shortcut to what Hampton Creek sees as the future of food. “We’re looking at the whole process,” Zigmond says of his data team, “trying to figure out what it all means and make better predictions about what is going to happen next.”

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Gilt's pre-emptive shipping is an experiment in data science

Gilt's pre-emptive shipping is an experiment in data science | What is Data Science | Scoop.it
Understanding what makes an item is also a challenge. Like color: That seems obvious, but it turns out not to be. Color, it depends on which definition -- what vendors call it, what Gilt calls it and what the National Retail Federation calls it. An item could have three colors assigned to it, and they could be very different. So, understanding specifics of high fashion attributes and so on.
Carla Gentry CSPO's insight:

Understanding what makes an item is also a challenge. Like color: That seems obvious, but it turns out not to be. Color, it depends on which definition -- what vendors call it, what Gilt calls it and what the National Retail Federation calls it. An item could have three colors assigned to it, and they could be very different. So, understanding specifics of high fashion attributes and so on.

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Big Data Analytics Vs. The Gut Check | TechCrunch

Big Data Analytics Vs. The Gut Check | TechCrunch | What is Data Science | Scoop.it
Data is more varied and fast-moving than ever, and analyzing it effectively now requires highly sophisticated software and machinery. But where does big data..
Carla Gentry CSPO's insight:

First, intuition guides analytics. Analytics insights rarely appear out of thin air. They’re the result of the application of numerical methods to test hypotheses and ideas that arise from intuition and observation. And intuition also guides the methods that the researcher uses to test these hypotheses. Which data is relevant? Which variables and transformations make sense? What are the likely relationships between cause and effect? Which models are appropriate?

 

Second, analytics informs intuition. Unsupervised modeling techniques can discern relationships and patterns in the data that wouldn’t be obvious from a superficial view or a human-sized sample of the data. In short, analytics can suggest avenues for exploration that wouldn’t be picked up by observation and might even be counter-intuitive.

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Lesson 2: NoSQL Databases are Good for Everything – Except Maybe this One Thing

Lesson 2: NoSQL Databases are Good for Everything – Except Maybe this One Thing | What is Data Science | Scoop.it
Summary:  In general, it is true that NOSQL databases can do everything that RDBMS can do.  And almost always when data is ‘big’ they can do it faster and chea…
Carla Gentry CSPO's insight:

Take the common example in which a deposit is made to fund A followed immediately by a transfer from fund A to fund B.  Since NOSQL relies on distributing the processing these two separate transactions may arrive and take action on fund A and fund B at different times depending on which of the three or four copies is being read.  The eventual consistency feature of NOSQL will ensure that all the points of storage agree, but there may be that lapse of a few milliseconds to a few minutes before all the reads from the system will agree.  If your customer immediately looks up his balance or initiates a third transaction, it could conceivably show different balances depending on which node is read at exactly which instant.

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Closing the Data-Driven Enterprise Adoption Gap

Closing the Data-Driven Enterprise Adoption Gap | What is Data Science | Scoop.it
To be successful in today’s marketplace, champions of data within the organization must be transformed through the adoption of BI tools to derive faster insight for themselves and their teams. Decisions should be guided equally by insight, delivered at the right time and place, in context, and embedded directly inside the applications they already work with every day. It’s precisely these distinct but important technology approaches that can deliver insight wherever you are and in the time you need it, ultimately improving your bottom line and, more broadly, making the world a better place. Plus, no one wants to sift through endless amounts of data to find the “needle in the haystack” insights.
Carla Gentry CSPO's insight:

Closing the BI adoption gap marks the greatest current challenge to businesses around the world. Looking back months or years from now, you may realize that adopting a BI tool for your organization in 2014 marked the moment your organization came to life. - See more at: http://data-informed.com/closing-data-driven-enterprise-adoption-gap/#sthash.VfUxMS9E.dpuf

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Rivo's Thought Leader Programme Strengthens Big Data and Sustainability Expertise on Environmental XPRT

Rivo's Thought Leader Programme Strengthens Big Data and Sustainability Expertise on Environmental XPRT | What is Data Science | Scoop.it
Helping businesses leverage and understand Big Data and Analytics
Carla Gentry is one of the most respected names in data mining and analytics, named one of Information Week’s 10 IT Leaders to follow on Twitter and Business Insider’s 25th Most Influential Tech Woman of 2014.  Most recently she spoke for the IBM Insight 2014 event in Las Vegas. Behind the scenes Carla is working closely with the Rivo Lab on their Analytics solution helping to provide Advanced Analytical capabilities to Rivo’s customer base by bridging the chasm between technology and business needs.
Carla Gentry CSPO's insight:

Rivo, the market leading cloud risk management platform for leading brands around the world has announced the recruitment of three world renowned experts in Data Analytics and Sustainability Reporting to an elite panel of Rivo Thought Leaders. The move by Rivo demonstrates how the global software player is offering value-added content and expertise to its customers.

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How fit is that Fitbit? - The Berkeley Science Review

How fit is that Fitbit? - The Berkeley Science Review | What is Data Science | Scoop.it
Technology used to be something external, we traveled to the office to use it – you sat  at your computer. Then technology became something we carried around – our cellphones, our laptops. Wearable tech used to be something of sci-fi and comic books – but now it is the norm. Among the most popular form …

Via Tictrac
Carla Gentry CSPO's insight:

Fitbit’s mantra is to make fitness part of your daily routine, allowing you to track your own data and use social media to “challenge” friends to meet their exercise goals. Fitbit has tapped into our need to be constantly connected by giving you live data – on your wrist – of how much you have walked each day. When you meet your daily goal, the Fitbit lets you know with a strong vibrating buzz. Not knowing exactly when that buzz is coming is like the variable reinforcement schedule that makes gambling so powerful.

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Make sure big data doesn't land you in big trouble

Make sure big data doesn't land you in big trouble | What is Data Science | Scoop.it
Because data arrives from so many different sources, often in real time, it is also important to validate the input so that malicious actors don't pollute the data set. Reavis's organisation recommends building systems that validate endpoints and detect malicious data feeds.
Carla Gentry CSPO's insight:
The more information you give away about that person, the less entropy you are left with. If you live in metro Vancouver (population 2.3 million), then that drops to 22 bits of entropy. And if someone can start cross-referencing that stuff with information from social media feeds and time series data, the bits will drop off fairly rapidly. Narayanan sliced away enough entropy from Netflix’s anonymised data set of user activity to identify several individual user records.
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Carla Gentry CSPO's curator insight, December 2, 3:09 PM

Another particular challenge of big data is that it is far more temporally focused than yesterday’s business intelligence systems. These were mostly static affairs, with snapshots of sales and other data updated on a regular, but far from real-time, basis. You could run reports on monthly sales data, for example.

Big data is different because it comes in from multiple sources much more frequently, some being updated in real or near-real time.

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Kicking Off A Data Science Project: 4 To-Dos - InformationWeek

Kicking Off A Data Science Project: 4 To-Dos - InformationWeek | What is Data Science | Scoop.it
"Data science has been around for decades, and it’s not just big data. I hear a lot of people clumping these two together like they go hand-in-hand, which I agree with to an extent," Gentry said via an email interview. "However, big data needs data science, but data science doesn’t necessarily need big data. Most of the data a typical company handles on a daily basis or houses internally is not big data."
Carla Gentry CSPO's insight:

Although big data might be most obviously linked to big organizations, data science can be useful to companies of all sizes and with all manner of datasets. "Even Facebook and Google break up or segment their data into workable pieces. Data science is big, small, structured, unstructured, messy, clean, et cetera," Gentry says.

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LinkedIn Accused Of Secretly Selling Your Professional Data To Potential Employers

LinkedIn Accused Of Secretly Selling Your Professional Data To Potential Employers | What is Data Science | Scoop.it
LinkedIn is only the latest social network to be accused of overstepping its bounds. Data mining has become a valuable tool for tech companies to learn more about their users and enhance their products. But the lack of clear-cut privacy protections and the growing amount of information companies collect are paving the way for legal troubles in the U.S. and abroad.
Carla Gentry CSPO's insight:

Additionally, employees — regardless of their field — are at risk for being firedbased on their social media presence. Social media blurs people’s personal and professional lives so much that employers often don’t distinguish between when employees make statements on their own time as a private citizen, and consider everything said online to be a reflection on the company itself. As a result, more employers are enforcing social media policies that give themleeway to fire employees based on what’s said online.

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How To Identify Fake Big Data Products

How To Identify Fake Big Data Products | What is Data Science | Scoop.it
But what does this have to do with Big Data? I guess you could access queries against Big Data repositories from DataRPM. But you can do that from a spreadsheet. Is a spreadsheet a Big Data technology? Is a pad of paper? In my view, DataRPM is obscuring its true value by claiming affinity with Big Data.
Carla Gentry CSPO's insight:

“To me, an analysis becomes a Big Data analysis when new, massive data sets are included and dots are connected to know more about patterns and outcomes,” said Ben Werther, CEO and Founder of Platfora. “When you combine the usually distinct silos of customer interactions, transactions, and machine data, you are in the realm of Big Data. We think that the critical challenge is making it possible for every business analyst to ask questions that matter right away without an IT bottleneck.”

 
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Big Data as a Disrupter - Thinking About Big Data Strategically

Big Data as a Disrupter - Thinking About Big Data Strategically | What is Data Science | Scoop.it

Google had the insight to use otherwise unexploited data (the links people built between web pages) to infer web page quality from social big data, and thereby deliver better search results than its competition. Any of the other search engines or big incumbent IT players of the time could have done this, but didn’t. They focused solely on the information available within the web pages, not the information implicit in the connections between web pages. They seemed blind to the absolutely free and accessible data in front of them. And because of that blindness, they essentially ceded the entire future of search to Google. 

From the perspective of our model, Google is an example of applying new algorithms to new kinds of big data.

Carla Gentry CSPO's insight:

To get a sense of how the model can be applied, let’s first touch on a few of the major success big data stories of the past, before the term big data was even cool, and examine how they map to our model. Success story number one is Google — arguably the most successful IT company of the last decade.

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Manchester Airports Group (MAG) selects Rivo’s Platform to centrally manage safety, security & sustainability - Rivo

Manchester Airports Group (MAG) selects Rivo’s Platform to centrally manage safety, security & sustainability - Rivo | What is Data Science | Scoop.it
MAG, our customer since 2012, originally selected Rivo to replace proprietary safety and risk assessment systems with a clear goal of achieving OHSAS 18001 accreditation within 2 years, which all the group’s airports have been accredited with.

Within 6 months, Rivo was successfully deployed and helped MAG achieve standardisation of data capture and reporting across the organisation.  MAG obtained a reduction in accident frequency rates, a decrease in timescales for follow-up on risk assessments, increased visibility of incidents and near-misses and greater employee engagement.
Carla Gentry CSPO's insight:

MAG, our customer since 2012, originally selected Rivo to replace proprietary safety and risk assessment systems with a clear goal of achieving OHSAS 18001 accreditation within 2 years, which all the group’s airports have been accredited with.

Within 6 months, Rivo was successfully deployed and helped MAG achieve standardisation of data capture and reporting across the organisation.  MAG obtained a reduction in accident frequency rates, a decrease in timescales for follow-up on risk assessments, increased visibility of incidents and near-misses and greater employee engagement.

 
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Hole in one: How Big Data can turn players into winners

Hole in one: How Big Data can turn players into winners | What is Data Science | Scoop.it
All these technologies are providing similar benefits across the business world. Indeed, the business side of sport has shown what’s possible. When the NCAA Men’s Basketball Tournament for college teams in the US kicked off in 2013, HP decided to see what data it could bring together from social media feeds. It analysed 500,000 Twitter posts using a range of hashtag and keyword information. This would then be collated to visualise when people tweeted, from where, whether they were positive or negative and the influence they had.
Carla Gentry CSPO's insight:

But to guarantee the accuracy, quality and safety of data, sports teams have had to invest heavily in protecting that information. A number of Premier League teams have already suffered as a result of hacks on their systems. Manchester City were embarrassed by a breach of their website over the summer, which led to a video being posted on their website related to the apparent transfer of FC Porto centre-back Eliaquim Mangala before the deal was done. Tottenham Hotspur and Barcelona have both had their Twitter accounts hacked in recent memory, indicating they weren’t adequately protecting their social media accounts.

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Be a data scientist for the love of the craft not the perks

Be a data scientist for the love of the craft not the perks | What is Data Science | Scoop.it
If you're not prepared to spend hours and hours by yourself just solving problems and writing code, this isn't the life for you.
Carla Gentry CSPO's insight:

If you're not prepared to spend hours and hours by yourself just solving problems and writing code, this isn't the life for you.

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NetAppVoice: We're Drowning In Data (And How To Build A Lifeline)

NetAppVoice: We're Drowning In Data (And How To Build A Lifeline) | What is Data Science | Scoop.it
In the big-data era, companies know more about their customers than ever. And they know it faster. Internet signals are all swimming just below the surface. And they can be netted with a click. But eyes are glazing over, as execs struggle over what to do with that flood of data.
Carla Gentry CSPO's insight:

Measurements like these may be helpful to the marketing team, but for the C-suite, they need to be taken to the last mile: How many of those impressions will produce leads that convert to customers and generate revenue? To be effective, marketers need to determine objectives-based measurements, such as lifetime customer value and customer retention, Rackley says. Then, instead of telling executives, “We think we can improve retention if we do this thing,” they can say, “With this much investment, we project we can improve customer retention by X% and generate $Y.”

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Bitcoin Phishing Click Rate Higher Than Regular Scams

Bitcoin Phishing Click Rate Higher Than Regular Scams | What is Data Science | Scoop.it
Bitcoin related phishing attacks get higher click through rates because users are curious about the new digital currency.
Carla Gentry CSPO's insight:

Curiosity Killed The Cash

The conclusion is simple. It means that in some cases the link pointing to the phishing website was accessed by users that did not even have a Bitcoin Wallet, highly likely out of curiosity about the digital currency. 

 

The phishing emails used a classic phishing strategy, a bogus alert of a suspicious sign-in attempt. To make sure that no Bitcoins are stolen, a password reset is recommended with a link to do that at the end of the message.

 

The messages claim to be from a Bitcoin related website called Blockchain.info and give a case number for the "incident", a classic social engineering tactic.

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