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Most Efficient Way To Manage Social Media Each Day [Infographic] - Bit Rebels

Most Efficient Way To Manage Social Media Each Day [Infographic] - Bit Rebels | Data Nerd's Corner | Scoop.it
If you are trying to manage your social media presence in the most efficient way while having a full-time job, you know it's tough. This guide can help you.
Carla Gentry CSPO's insight:

Few people have been able to manage the overlapping processing time, and even fewer have been successful at it. If you don’t have the time to manage your social media presence for 8 hours a day then what is the most efficient way of doing it?

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Big Data, IOT and Security - OH MY!

Big Data, IOT and Security - OH MY! | Data Nerd's Corner | Scoop.it
Why should I – as a data scientist or analyst be worried about security, that’s not really part of my job is it? Well if you are a consultant or own your own business it is! Say, you download secure data from your clients and then YOU get hacked, guess who is liable if sensitive information is leaked or gets into the wrong hands? What if you develop a platform where the client’s customers can log in and check their accounts, credit card info and purchase histories are stored on this system, if stolen, it can set you up for a lawsuit. If you are a corporation, you are protected in some extents but what if you operate as a sole proprietor – you could lose your home, company and reputation. Still think security when dealing with big data isn’t important?
Carla Gentry CSPO's insight:
Organizations need to get better at protecting themselves and discovering that they’ve been breached plus we, the consultants, need to do a better job of protecting our own data and that means you can’t use password as a password! Let’s not make it easy for the hackers and let’s be sure that when we collect sensitive data and yes, even the data collected from cool technology toys connected to the internet, that we are security minded, meaning check your statements, logs and security messages - verify everything! When building your database, use all the security features available (masking, obfuscation, encryption) so that if someone does gain access, what they steal is NOT usable!
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MIT Computer Scientists Demonstrate the Hard Way That Gender Still Matters | WIRED

MIT Computer Scientists Demonstrate the Hard Way That Gender Still Matters | WIRED | Data Nerd's Corner | Scoop.it
“We’re 3 female computer scientists at MIT, here to answer questions about programming and academia. Ask us anything!” we wrote for our Reddit Ask Me Anything session last Friday. And then, boom:
“WHY DOES IT MATTER THAT YOU’RE FEMALE?”
“WHY DID YOU PUT GENDER IN THE TITLE?”
“WHY SHOULD YOUR GENDER MATTER IF YOU’RE TALKING ABOUT RESEARCH?”
Dozens of questions like these were interspersed with marriage proposals and requests to “make me a sandwich” in our AMA. We had intended for the AMA to be a chance to answer questions about what our lives are like as PhD students at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), and what we could do to get more young people excited about programming.

The AMA became, to borrow one Reddit commenter’s phrase, “a parody of what it’s actually like to be a woman working in a STEM field.”
Carla Gentry CSPO's insight:

As computer science PhD students, we were interested in fielding questions about programming, academia, MIT CSAIL, and how we got interested in the subject in the first place. 

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Fallacy of Rational Prerequisite & My Fruitless Existence

Fallacy of Rational Prerequisite & My Fruitless Existence | Data Nerd's Corner | Scoop.it
There have been comparisons between data science and computer science. Some have suggested that computer science is not a real science, and data science should be regarded similarly. I consider this an interesting debate. The divisiveness can easily cause a conversation to descend into tribalism and theocracy. The fight for epistemological territory ostensibly scientific has really been about controlling the recognition of data in the sense of asserting ownership. Consider a comparison to patriotic or religious fanaticism. "This is an evil act." "We are good people." "This is unacceptable in a civilized society." So sayeth nations that direct enormous budgetary resources to its military - waging war constantly. I realize that these seem to be declarations of ideology. I suppose it would be necessary to consider the situation from the perspective of my own duties where I make observations based on established criteria and personal experience. "This is a mistake." "This will cause problems if not corrected." "This has consequences." I believe if we examine how these assertions define the placement of data as objects of relevance, it would be apparent that underlying intent is to impose a belief system or rational structure over the construction of our lived realities. It is an intellectual incursion - viral in nature - sometimes purely functional and at other times premised on the need for authority and centralized power. We find ourselves basing our handling of material concerns over immaterial beliefs. Through capital, we give life to dogma. Yet dogma doesn't actually give us a return on investment. When we remove the tribalism and theocracy from the discussion, it should become apparent that these themes are philosophical. They can also be rather destructive or at least extremely distracting.
Carla Gentry CSPO's insight:

I believe that people have a tendency to rationalize situations, which I suggest means providing a plausible or convincing explanation that is intellectual in nature. A bear touching a fire might learn to avoid it in the future; most of us would agree that it is reasonable to do so. But the bear's behaviors are rooted in its immediate physical needs. Humans on the other hand perform and persist with behaviors without the need for physical stimuli. But we often require rational explanation to substantiate our behaviors: 

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What Your Business Can Learn about Leveraging Big Data From Netflix, Eloqua and the 2008 Election

What Your Business Can Learn about Leveraging Big Data From Netflix, Eloqua and the 2008 Election | Data Nerd's Corner | Scoop.it
Netflix originally started as a mail-order to alternative to Blockbuster. But at its heart, Netflix is a data play. The selection queue gave Netflix insight into its users’ viewing habits and enabled it to recommend movies and TV shows its customer might enjoy. That capability has been expanded and made more immediate now that Netflix streams entertainment to its viewers. 
Carla Gentry CSPO's insight:

Eloqua is another startup that rode big data to success: This marketing-automation software company was sold to Oracle in 2013 for $957 million. One of the secrets to Eloqua’s success was its software-as-a-service model. Because it hosted its solution online, Eloqua had access to rich data on how its customers were using the product, which gave the company tremendous insight into how to serve individual customers and how to make the software better.

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The Future of Privacy

The Future of Privacy | Data Nerd's Corner | Scoop.it
“We have seen the emergence of publicy as the default modality, with privacy declining. In order to ‘exist’ online, you have to publish things to be shared, and that has to be done in open, public spaces.”
The terms of citizenship and social life
Carla Gentry CSPO's insight:

Moreover, personal data are the raw material of the knowledge economy. As Leah Lievrouw, a professor of information studies at the University of California-Los Angeles, noted in her response, “The capture of such data lies at the heart of the business models of the most successful technology firms (and increasingly, in traditional industries like retail, health care, entertainment and media, finance, and insurance) and government assumptions about citizens’ relationship to the state.”

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Moving from Data Science to Data Literacy — Medium

Moving from Data Science to Data Literacy — Medium | Data Nerd's Corner | Scoop.it
The disciplines under the social sciences and the humanities must be equals with math and computer science to unlock greater understanding.
Carla Gentry CSPO's insight:
To deepen the benefits of Big Data, we must put the social sciences and the humanities on equal footing with math and computer science.
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Data Mining and Predictive Analysis

Data Mining and Predictive Analysis | Data Nerd's Corner | Scoop.it
Data collection and curing is the core foundation of most businesses. Database building thus is an important function and activity where enterprises invest heavily. With information now available o...
Carla Gentry CSPO's insight:

Once a database is compiled, it needs to be cleaned, analysed and potential connections need to be built. This process involves filtering the relevant data and identifying the possible predictors. Data Exploration also sets a premise for preliminary feature selection to manage number of variables. This data is then prepared for statistical analysis using a wide variety of graphical and statistical parameters. This helps identify the most relevant variables and setups the predictive models to be built.

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Angela Kok's curator insight, December 18, 11:20 PM

3 steps to data mining and predictive analysis.

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Sponsored post: Big Data and Intuition: The Future of Marketing

Sponsored post: Big Data and Intuition: The Future of Marketing | Data Nerd's Corner | Scoop.it
Technology isn’t only getting faster, it’s getting smarter. Computers are able to recognize and learn from patterns and make changes in real-time. Their improved analytic and decision-making abilities now allow them to outperform humans in areas such as medical diagnosis and customized marketing campaigns.
Carla Gentry CSPO's insight:

However, it’s hard for marketers to embrace data analysis when they’ve trusted their own gut to fuel decisions for so long. It’s a point of pride for many.  The problem is, the strategy frequently fails. A 20 year studyof political pundits found that they were only as accurate as a coin toss, suggesting that successful “intuitive” decisions are often a lucky guess.

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The big data cloud challenge

The big data cloud challenge | Data Nerd's Corner | Scoop.it
Intel and its partners here say that enterprises can now run Cloudera Enterprise on the CenturyLink Cloud powered by Intel Cloud Technology for what should appear to be fast deployment and fast time to insight i.e. what we actually want from big data analytics in the cloud. What we should also get (in theory) here is flexibility to move between cloud and on-premises solutions.
Carla Gentry CSPO's insight:

Why do we need analytics at this level? Because we want to analyze the ever-increasing volume and variety of big data that’s pouring in from social media, clickstreams (see below), videos, sensors and more

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Making sense through order

Making sense through order | Data Nerd's Corner | Scoop.it
Qian and Aslin developed a rational computational model to explain how people infer such hidden structures in sequential data, which they refer to as "bundles." They call it the Hibachi grill process (HGP) mixture model, named for a popular Japanese-style dining experience. Imagine a restaurant at which one entrée is the primary offering at each table and customers are seated based on their meal preferences. The owner is trying to maximize customer satisfaction and staff efficiency by having customers sit at the tables of their preferred dishes. The HGP mixture model extends the metaphor to include the assumption that restaurant customers typically arrive as groups of friends who have similar tastes and would want to sit at the same table. The model uses mathematical computations in an attempt to identify those hidden groups of friends from a stream of customers by relying on the stimulus order—that is, the order in which the customers enter the restaurant. The additional feature—that different groups of friends may share the same table—gives the model its name - a resemblance to the typical seating arrangement in many Hibachi grill restaurants in the US.
Carla Gentry CSPO's insight:

It is unclear whether people should discover bundles from order in every situation. The Ebola crisis offers a real-world scenario. "If, for example, a person heard about three consecutive patients at a medical clinic who were diagnosed with the Ebola virus, he or she might suspect a community-wide epidemic," said Aslin. But, it's possible that the information conveyed by the patient order was not meaningful. It may be that the three patients were from the same household and contracted the disease because they were living together.

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In Big Data, Shepherding Comes First

In Big Data, Shepherding Comes First | Data Nerd's Corner | Scoop.it
Big data is increasingly moving into the mainstream, as companies in every industry begin to combine an abundance of digital data with smart software to analyze it. It is a potential gold mine for software makers, with analysts predicting torrid growth for the market overall.

But building big data businesses is proving to be anything but a get-rich-quick game, and to require both agility and patience.
Carla Gentry CSPO's insight:

“Everybody is scrambling to take revenue where they can get it,” said Jonathan Gray, chief executive of Cask, which was previously Continuuity.

The challenge is similar for major technology corporations pursuing the big data market, but it is most acute for start-ups, which lack the financial ballast of the tech giants.

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What Artificial Intelligence Is Not

What Artificial Intelligence Is Not | Data Nerd's Corner | Scoop.it
Finally AI, like all computer programs, are ultimately controlled by humans. Of course AI can be designed with malicious intent and weaponized like nuclear or biological technology, but that’s not a fault of the science but of ourselves.
Carla Gentry CSPO's insight:

While Elon Musk is a personal hero of mine, and a genius on so many levels, his recent comments on artificial intelligence have been a little less than brilliant. He mentions that AI is more dangerous than nuclear weapons and that we may summon an AI “demon” (his words, not mine). My only explanation is that he must have fallen asleep watching Terminator.

 

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How to Become A Numbers Person in Journalism | Mediashift | PBS

How to Become A Numbers Person in Journalism | Mediashift | PBS | Data Nerd's Corner | Scoop.it
Another thing to remember in your quest for numeracy is that you must read. Not just essays and tutorials on the Internet; you must also read books. That’s how the pros do it. Some fabulous books that are written for ordinary people and journalists include: Sarah Cohen’s Numbers in the Newsroom; Jordan Ellenberg’s How Not to be Wrong; John Allen Paulos’ Innumeracy or A Mathematician Reads the Newspaper; Joel Best’s Damned Lies and Statistics.

As you read, rid yourself of math anxiety. Your math education has probably prepared you better than you think. Math (especially journalism math) is not an unapproachable discipline only for elites, as mathematician John Allen Paulos reminds us in his book Innumeracy. He writes, “Almost everybody can develop a workable understanding of numbers and probabilities, of relationships and arguments, of graphs and rates of change and of the ubiquitous role these notions play in everyday life.” Most of the battle for becoming numerate is confidence. You will make mistakes, sure. But you are writing journalism, which is a collective rather than a solo venture. Reach out to people who can help you validate your findings. Preferably before publication.
Carla Gentry CSPO's insight:

Of course, you’ll want to validate your potential finding with some shoe leather reporting. Data is a source, and it requires confirmation just like controversial information from any other source. You want to talk to the bridge inspection authorities to give them a chance to respond or clarify. Verify that the bridge was not inspected more recently than the data suggests. Talk to a bridge expert to find out why the bridge wasn’t inspected, or what the consequences could be if a bridge is in disrepair. You may also want to talk to other data journalists, who can be found on an email list such as NICAR or ddj.

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2015 will mark the beginning of the end for email

2015 will mark the beginning of the end for email | Data Nerd's Corner | Scoop.it
Email isn’t going to die in 2015, or anytime in the foreseeable future, but this could be the year that alternatives finally start to gain traction.
Carla Gentry CSPO's insight:

Perhaps the biggest sign yet of the change at hand comes from Germany, which has called for an “anti-stress regulation” that would, among other things, ban employers from contacting employees after hours. Chancellor Angela Merkel has criticized the law and stopped it from moving forward for now, but German leaders have long been concerned about the growing tendency for technology to allow work to encroach on employees’ private lives.

 
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Your Data Science Portfolio: Math Skills Don't Matter

Your Data Science Portfolio: Math Skills Don't Matter | Data Nerd's Corner | Scoop.it
A Data Scientist is a data pipeline plumber. Analytics are icing, not cake.
Carla Gentry CSPO's insight:

As I've said before, one of the beautiful things about working with data is that it provides concrete context. An employee that intimately understands the context of the company's data is indispensable. They are able to take one glimpse at a report and say "something's wrong here", cutting through hours/weeks of an analyst's work. Statistical models are built on pyramids of assumptions, and assumptions are famously brittle. As the sun sets on the Data Science marketing hype, the success of your your transition into a new position will depend on how well you understand the intricacies of how your industry's data relates to the real world.

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Nasa just emailed a wrench to space (Wired UK)

Nasa just emailed a wrench to space (Wired UK) | Data Nerd's Corner | Scoop.it
Made In Space, the California company that designed the 3D printer aboard the ISS, overheard Wilmore mentioning the need for a ratcheting socket wrench and decided to create one. Previously, if an astronaut needed a specific tool it would have to be flown up on the next mission to the ISS, which could take months.
Carla Gentry CSPO's insight:

This isn't the first 3D printed object made in space, but it is the first created to meet the needs of an astronaut. In Novemberastronauts aboard the ISS printed a replacement part for the recently installed 3D printer. A total of 21 objects have now been printed in space, all of which will be brought back to Earth for testing.

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Best solution to a problem: data science versus statistical paradigm

Best solution to a problem: data science versus statistical paradigm | Data Nerd's Corner | Scoop.it
One commenter (whose name is R) wrote that this methodology is indeed a bootstrap-like approach without re-sampling that happens to be estimating the distribution of the wrong quantity. Since it produces the same results as classic stats, does it mean that classic stats are also wrong? And man, what a lot of jargon used in R's sentence, whoever this guy is. Not only jargon, but statistical science not found in any standard statistics textbook. So how is the layman supposed to know about it?
Carla Gentry CSPO's insight:

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. Some have tried to create laws about appellations, but that's the wrong approach. In the new language that I promote, called New English, anyone can call herself lawyer, doctor, married, data scientist, bank - you name it. Just do your due diligence before hiring or talking to someone who claims to have some credentials, whatever these credentials might be.

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Big Data Analytics: Time For New Tools - InformationWeek

Big Data Analytics: Time For New Tools - InformationWeek | Data Nerd's Corner | Scoop.it
A recent Forrester report declared that Hadoop is "no longer optional" for large enterprises. Our data suggests that train hasn't left the station just yet: Just 4% of companies use Hadoop extensively, while 18% say they use it on a limited basis, according to our just-released 2015 InformationWeek Analytics, Business Intelligence, and Information Management Survey. That is up from the 3% reporting extensive use and 12% reporting limited use of Hadoop in our survey last year. Another 20% plan to use Hadoop, though that still leaves 58% with no plans to use it.
Carla Gentry CSPO's insight:

But there's no doubt that interest in Hadoop is rising. The top draw is the platform's "ability to store and process semi-structured, unstructured, and variable data," cited by 31% of the 374 respondents to our survey involved with information management technology. Another 30% cited Hadoop's ability to handle "massive volumes of data," while 25% said it's Hadoop's "lower hardware and storage scaling costs" as compared to conventional relational database management systems.

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PRESS RELEASE: Rivo's Thought Leader Programme Strengthens Big Data and Sustainability Expertise - Rivo

PRESS RELEASE: Rivo's Thought Leader Programme Strengthens Big Data and Sustainability Expertise - Rivo | Data Nerd's Corner | Scoop.it
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:

LONDON, UK: 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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10 data science predictions for 2015

10 data science predictions for 2015 | Data Nerd's Corner | Scoop.it
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Buzz: Business News | Announcements | Events | RSS Feeds
Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers
Carla Gentry CSPO's insight:

These predictions were published by the International Institute for Analytics (IIA). They produced a nice infographics, featured below, and re-tweeted many times by various bloggers, using the hash tag#2015Analytics. Other interesting predictions include those by Tableau, those by Pivotal, as well as my own predictions.

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IT Hiring, Budgets In 2015: 7 Telling Stats - InformationWeek

IT Hiring, Budgets In 2015: 7 Telling Stats - InformationWeek | Data Nerd's Corner | Scoop.it
No doubt some of today's technologies -- such as improved automation that allows for lights-out, remotely managed, highly virtualized data centers -- will eliminate some IT jobs. Virtualization continues to let IT organizations spend less time manipulating physical boxes. But as Choice Hotels International CIO Todd Davis told us recently, increased complexity in the data center also has made top-flight infrastructure professionals among the hardest IT pros to find.
Carla Gentry CSPO's insight:

But today's rising IT demand isn't just about project volume -- generating 500 BI reports in the coming year instead of 450 this year. It's about IT doing entirely new things, the kind of creative work that doesn't just lower costs but also helps companies grow. It's about mobile apps that build new customer ties, data analysis that spots new opportunities, technology-powered services that drive new revenue.

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The Internet Of Things' Best-Kept Secret

The Internet Of Things' Best-Kept Secret | Data Nerd's Corner | Scoop.it
So today we have smart products and software is eating the world. But why connect the product to the Internet? “The number one reason,” says Heppelmann, “is to service them better. The number two reason is to operate them better.” But the sum of the changes is greater than the parts, resulting in improved, even new, business models. It’s a business transformation driven by digitization.
Carla Gentry CSPO's insight:

The evolution of PTC’s business is a great case study of how digitization is eating the world. For Heppelmann, taking a company known as “a CAD company, maybe a CAD and PLM company” to the suddenly-hot Internet of Things (IoT) market “is pretty logical.” He takes me on a 30-year journey, starting with the founding of the company by Sam Geisberg: “When you hear about 3D printers, think Sam Geisberg. The 3D technology that’s in use today is almost identical to what he invented—the first line of code he wrote is still running out there every day.”

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Mario Lenz's curator insight, December 17, 6:51 AM

Number one reason for #IoT is to service products better.

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2014: A Year in Which Big Data Dominated Every Conversation

2014: A Year in Which Big Data Dominated Every Conversation | Data Nerd's Corner | Scoop.it
One of the primary drivers for hybrid cloud adoption this past year was big data analytics. With so many big data analytics projects underway in all business sectors, the agility, scalability, and elasticity of the cloud was a natural fit.

On-demand Hadoop services, such as Elastic Map Reduce, made it simple and cost effective for organizations to prototype and operationalize big data processing and analysis. Cloud storage solutions like Amazon S3 provided a unique storage platform for data processing and archiving, and offered organizations a viable alternative to growing existing on-premise SAN infrastructure. And one of the true stars of the show was Amazon Redshift. The combination of low cost, no maintenance elasticity and scalability have enticed customers enough to make it the fastest growing service on AWS.
Carla Gentry CSPO's insight:

In 2014, there was a strong move to hybrid cloud solutions and hybrid IT organizations. Migration to the cloud by the enterprise was in full force this year. This migration included “hybrid cloud,” a concept in which businesses used both private and public cloud-based solutions that provide the best of both worlds. This year, we saw hybrid solutions offer the much-needed storage and flexibility of the cloud, as well as the speed and safety of having internal networking where it matters. - See more at: http://data-informed.com/2014-year-big-data-dominated-every-conversation/#sthash.uKWa2es7.ybeTGP5G.dpuf

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More Businesses Are Diving Into Big Data Analytics

More Businesses Are Diving Into Big Data Analytics | Data Nerd's Corner | Scoop.it
Survey analysis points to "the newness of the technology and the skills required for tactical investment" as factors delaying full-scale adoption of big data analytics. The lack of infrastructure investment coincides with increased experimentation and the use of cloud services as businesses probe the idea of analytics for business insights.
Carla Gentry CSPO's insight:

Midsize businesses considering big data analytics are a key audience for cloud services, particularly during the early analytics experimentation phases. As businesses of all sizes explore the possibility of cloud analytics, service providers that are motivated to develop cost-effective solutions may be able to meet this demand. The availability of cloud analytics options and the skills to manage data migration, transformation and integration can tilt midsize businesses toward cloud offerings.

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Data science: 'Machines do analytics. Humans do analysis' | ZDNet

Data science: 'Machines do analytics. Humans do analysis' | ZDNet | Data Nerd's Corner | Scoop.it
Booz Allen has a team of 500 devoted to data science projects and 50 of them are "national treasures," says Sullivan. Those elite 50 data scientists have worked on multiple projects in many industries and have all the traits required for asking the right questions needed to transform business.

"These people are curious and relentless in the face of failure," Sullivan said. "They keep pushing and no matter and they think they can contribute no matter how big the problem is." For instance, a team may fail 340 times before finding the pattern that means something. You can't get disappointed easily.

Via Don Dea
Carla Gentry CSPO's insight:

Sullivan isn't big on analytics technology that serves as a magic bullet to data science. No machine can be a miracle cure. Humans have to find the patterns, ask the right questions and make the connections in the data. "Machines do analytics," explained Sullivan. "Humans do analysis." Computers are good at detail and examining the past, but real data science requires imagination and cognitive ability.

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Don Dea's curator insight, December 10, 1:06 AM

No machine can be a miracle cure. Humans have to find the patterns, ask the right questions and make the connections in the data. "Machines do analytics," explained Sullivan. "Humans do analysis." Computers are good at detail and examining the past, but real data science requires imagination and cognitive ability.

Luca Naso's curator insight, December 11, 11:33 AM

In Data Science "talent" means to be "relentless in the face of failure"

 

Insights (aka Big Data Value) builds on Big Brains:

No machine can be a miracle cure. Humans have to find the patterns, ask the right questions and make the connections in the data.

Fàtima Galan's curator insight, December 17, 3:48 AM

"Data science is a team sport and you need a diverse team to explore multiple angles."

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Disney Research builds computer models to analyze play in pro basketball and soccer

Disney Research builds computer models to analyze play in pro basketball and soccer | Data Nerd's Corner | Scoop.it
In a new study, researchers at Disney Research Pittsburgh showed they could use player tracking data from more than 600 basketball games of the 2012-13 NBA season to build models that can make accurate in-game predictions of what each player is likely to do next in a game situation: pass or shoot.
Carla Gentry CSPO's insight:

In a separate study using a season's worth of ball and player tracking data from a professional soccer league - more than 400 million data points - Disney Research Pittsburgh researchers performed a different type of analysis that looked at team behavior rather than individual players. They showed their system could accurately detect and visualize team formations - well enough that they could identify teams based just on their style of play 70 percent of the time.

Read more at: http://phys.org/news/2014-12-disney-pro-basketball-soccer.html#jCp

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