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antropologiaNet, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
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Travail et travailleurs de la donnée - #Algopol | #datascience #methods

Travail et travailleurs de la donnée - #Algopol | #datascience #methods | e-Xploration | Scoop.it
Le questionnement scientifique qui anime le projet ALGOPOL voudrait comprendre la structure des liens sociaux existant au sein de réseaux égocentrés à partir du contenu des échanges et des liens partagés sur Facebook. Les interactions sur cette plateforme se déploient-elles différemment, avec une énonciation différente, autour de contenus partagés différents, selon les segments du réseau social mobilisés ? A-t-on des conversations différentes avec les liens « forts » et les liens « faibles » ? Les objets informationnels mis en partage sont-ils les mêmes selon la forme et la structure de la sociabilité numérique des individus ? Chercher à répondre à ces questions requiert des données fines et précises que les méthodes d’enquête traditionnelle ont beaucoup de difficulté à fournir [11].
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#BigData, new epistemologies and paradigm shifts | #socialscience #DH

#BigData, new epistemologies and paradigm shifts | #socialscience #DH | e-Xploration | Scoop.it
luiy's insight:

Whilst Jim Gray envisages the fourth paradigm of science to be data-intensive and a radically new extension of the established scientific method, others suggest that Big Data ushers in a new era of empiricism, wherein the volume of data, accompanied by techniques that can reveal their inherent truth, enables data to speak for themselves free of theory. The empiricist view has gained credence outside of the academy, especially within business circles, but its ideas have also taken root in the new field of data science and other sciences. In contrast, a new mode of data-driven science is emerging within traditional disciplines in the academy. In this section, the epistemological claims of both approaches are critically examined, mindful of the different drivers and aspirations of business and the academy, with the former preoccupied with employing data analytics to identify new products, markets and opportunities rather than advance knowledge per se, and the latter focused on how best to make sense of the world and to determine explanations as to phenomena and processes.

 

http://bds.sagepub.com/content/1/1/2053951714528481.full

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Machine Learning #WorkFlow | #datascience #bigdata

Machine Learning #WorkFlow | #datascience #bigdata | e-Xploration | Scoop.it
So far, I am planning to write a serie of posts explaining a basic Machine Learning work-flow (mostly supervised). In this post, my target is to propose the bird-eye view, as I'll dwell into details at the latter posts explaining each of the components in detail. I decide to write this serie due to two reasons; the first reason is self-education -to get all my bits and pieces together after a period of theoretical research and industrial practice- the second is to present a naive guide to beginn

Via ukituki
luiy's insight:

Each box has a color tone from YELLOW to RED. The yellower the box, the more this component relies on Statistics knowledge base. As the box turns into red[gets darker], the component depends more heavily on Machine Learning knowledge base. By saying this, I also imply that, without good statistical understanding, we are not able to construct a convenient machine learning pipeline. As a footnote, this schema is changed by post-modernism of Representation Learning algorithms and I'll touch this at the latter posts.

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Introducing the Living With #Data series | #personalData #algorithms

Introducing the Living With #Data series | #personalData #algorithms | e-Xploration | Scoop.it
A field guide to the data and algorithms that shape our world
luiy's insight:

Literacy before legibility

 

Ads may seem innocuous, and many of us have learned to ignore them. But these ads might be some of the clearest signals we have about where our data flows and how it could be used in other contexts.

 

Each click is an input that goes in one end of the algorithmic black box, and the rest of our online experience comes out the other. Data is making our behaviors, habits and interests more legible to corporations and governments. But most of the time, that data is hidden from us unless we go digging for it. Even then, we have to know how and where to look. And we almost never get to see how the algorithms work, based on whatever parameters, features, weights and preferences engineers design into the system. That’s all proprietary — the secret sauce.

 

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#Bigdata, language and the death of the theorist | #DH #algorithms

#Bigdata, language and the death of the theorist | #DH #algorithms | e-Xploration | Scoop.it
Plenty of people have foreseen the death of the scientific theory at the hands of big data analysis, but when computers become good enough to understand literature, art and human history, will it spell the end for the humanities academic?
luiy's insight:

A lot has been written about the ways that big data has changed scientific enquiry, but as supercomputers increase in power and the tools to use them become less obtuse, whole new academic disciplines are beginning to feel the benefits of crunching data.

 

Believe it or not, some people even think we can forecast the future with big data. Predicting world-changing events is a possibility, some claim, if you treat society and history like a big data problem. It's how big data analyst Kalev Leetaru found where Osama bin Laden had been hiding, in a way.

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New #tool makes online #personaldata more transparent | #privacy

New #tool makes online #personaldata more transparent | #privacy | e-Xploration | Scoop.it
The web can be an opaque black box: it leverages our personal information without our knowledge or control. When, for instance, a user sees an ad about depression online, she may not realize that she is seeing it because she recently sent an email about being sad. Roxana Geambasu and Augustin Chaintreau, ...
luiy's insight:

The tool for revealing personal data use on the Web. It reveals which specific data inputs (such as emails) are used to target which outputs (such as ads). It is general and can track data use both within and across arbitrary Web services. The key idea behind XRay is to detect targeting through black-box input/output correlation. XRay populates a series of extra accounts with subsets of the inputs and then looks at the differences and commonalities between the outputs that they get in order to obtain correlation. This mechanism is effective at detecting certain types of data uses, though not all. For its details, please refer to our research paper, which will appear in August at USENIX Security 2014, a top systems security conference.


http://xray.cs.columbia.edu/

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Why #BigData Requires the #SocialSciences

Why #BigData Requires the #SocialSciences | e-Xploration | Scoop.it
Courtesy of Hallam Stevens | Smart Data Collective This is the first in a series of posts that will examine the big data phenomenon from the point...
luiy's insight:

The social sciences have spent a lot of time thinking about the relationship between technology and society. Big data are, of course, part of technological system. They emerge from computers, databases, and the World Wide Web. But, big data are not only a technological phenomenon. Data are collected for particular purposes (eg. because they are valuable) by and for particular individuals or groups. In other words, which data is collected, how it is collected, from where and who, and how much, depends not just on technological capability but on the social and political interests at stake (a point that has recently been recognized by the White House in its report and in the conference on 'The social, cultural, and ethical dimensions of Big Data').

 

This is typical of other kinds of technological systems. The form taken by televisions, bridges, power tools, or missiles doesn’t just depend on technical or engineering questions, but on social and political interests. If we want to understand why these things are the way were are, we have to understand them not merely as technological objects, but also as ‘social’ objects. All but the most superficial analysis of data is going to need to take account of how, why, and for what reason a particular set of data came to be. 

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Leoncio Lopez-Ocon's curator insight, May 23, 2014 2:38 AM

Cómo el uso de los big data requiere el conocimiento de las ciencias sociales

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Humanidades digitales y #BigData | #dh

Humanidades digitales y #BigData | #dh | e-Xploration | Scoop.it
Artículo de Juan Luis Suárez sobre la futura revolución de los grandes datos.

Via A Petapouca, Pierre Levy
luiy's insight:

Las humanidades digitales han comenzado a emitir señales acerca de su primera madurez. Éstas se pueden detectar en las agrupaciones de intereses y métodos que ya se distinguen por encima del ruido de los exploradores. Por un lado, parte de la práctica humanística se ha centrado en el uso y construcción de nuevos métodos de comunicación de la producción cultural y de la investigación humanística, desde blogs y tuits hasta sitios webs y libros electrónicos. Por otro lado, los más apegados a los textos han hecho del marcado de los mismos y de sus estándares –Text Encoding Initiative– uno de los campos más prolíficos de producción académica, aunque los avances en procesamiento de lenguaje natural y en machine learning comienzan a cuestionar si el esfuerzo ha valido la pena. En tercer lugar, hay quienes usando una variedad de tecnologías están valiéndose de todo el poder de la computación para, mezcladas con conceptos de sus disciplinas históricas, filológicas, o antropológicas, comprender mejor algunos de los problemas tradicionales de esas disciplinas.

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Catherine Pascal's curator insight, March 26, 2014 6:52 AM

Important . Merci !

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#trends : #Top10 Innovative Companies in #BigData 2014

#trends : #Top10 Innovative Companies in #BigData 2014 | e-Xploration | Scoop.it
According to one projection, the sales of big-data-related products and services grew to more than $18 billion in 2013. Companies can now map your...
luiy's insight:

1. GE

For harnessing data from its planes and trains to power a new Industrial Internet, potentially saving billions. General Electric is best known for its machine making, but it’s gotten smart and branded itself as a big-data company, too, by pushing its vision for an “Industrial Internet”

 

2. KAGGLE

For feeding its DIY data scientists cash-prize challenges (then molding them into a consulting biz). Now more than ever, organizations are turning toward data insights to make big decisions, and with its battalion of 150,000 data scientists, no one is better poised to take advantage of the shift than Kaggle. It farms out complex “data challenges” that come with cash prizes

 

3. AYASDI

For using a visual approach to take the guesswork out of big data. Instead of mining petabytes of info to solve a problem, customers of DARPA-funded spinout Ayasdi

 

. MOUNT SINAI ICAHN SCHOOL OF MEDICINE

For embracing data scientists and supercomputers to build the hospital of the future. The New York City hospital is bringing on top Silicon Valley talent to build a facility that will map patients’ genomes to predict diseases, reduce the number of average hospital visits, and streamline electronic medical records.

 

6. THE WEATHER COMPANY

For analyzing millions of local climates to predict how shoppers’ habits sway with the weather. It’s more than just a weather channel. By analyzing the behavior patterns of its digital and mobile users in 3 million locations worldwide

 

7. KNEWTON

For forging alliances to make millions of students smarter, from adaptive­-learning e­books to personalized English­ language training courses. Any teacher can walk students through a course. But to pinpoint and develop the specific problem areas of each student—in classrooms that are already at capacity—is a tough undertaking, which is where Knewton steps in

 

8. SPLUNK

For providing businesses with hundreds of homegrown apps to sniff out error files and keep things humming. After going public in 2012, Splunk has continued its explosive growth as a pure-play leader in the big-data space. It pulled in $200 million in revenue in 2013, thanks to new customers like T-Mobile, the U.S.

 

9. GNIP

For expanding its service to let customers dive through every social media stream available. Gnip’s service, which lets customers monitor and parse through social media streams by attributes like keywords, trends, and locations, has grown even more powerful over the past year. In addition to data from Facebook, YouTube, and Google+, and access to Twitter’s full historical stream of tweets

 

0. EVOLV

For mining employee performance to help stanch turnover and upend HR. Big data is also changing the way companies hire and manage their workforces. Like other HR software, Evolv helps employers better understand employees and job candidates by comparing their skills, work experience, and personalities. 

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#BigData Investment Map 2014 | #dataviz #SNA via @furukama

#BigData Investment Map 2014 | #dataviz #SNA via @furukama | e-Xploration | Scoop.it

by BENEDIKT KOEHLER on 1. FEBRUAR 2014

luiy's insight:

Here’s an updated version of our Big Data Investment Map. I’ve collected information about ca. 50 of the most important Big Data startups via the Crunchbase API. The funding rounds were used to create a weighted directed network with investments being the edges between the nodes (investors and/or startups). If there were multiple companies or persons participating in a funding round, I split the sum between all investors.

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[fiction N°7] La Plateformisation a commencé - Les transports du futur | #bigdataPerso #CI

[fiction N°7] La Plateformisation a commencé - Les transports du futur | #bigdataPerso #CI | e-Xploration | Scoop.it
« La valeur de la donnée disparaît quand on parvient à lui en donner une. Il faudra fixer la valeur en remontant le temps.

Via Pierre Levy
luiy's insight:

Inspiré par le programme Datact, Mathieu, talentueux programmeur, avait réalisé, avec une équipe internationale, le premier « révélateur stockeur de données personnelles » et avait proposé plusieurs théories majeures sur les modèles d’échanges de richesses des données. Le « révélateur stockeur » était un outil citoyen conçu pour comprendre cet équilibre risques/bénéfices. Il affichait clairement pour qui, pourquoi les GAFA et autres plateformes marchandes utilisaient vos traces numériques ? à qui étaient-elles fournies ? pour quels bénéfices ? Puis vous pouvez décider de les aspirer intégralement, pour les stocker sur des serveurs sécurisés et gérer votre propre compte de data, votre Big Data perso. Vous décidez alors qui les utilise et pour quels usages, comme Green Button le proposait déjà en 2013 dans le domaine de l'énergie. Des usages philanthropiques de vos données sont également possible (comme ce don d'activité physique pour ceux qui souffrent de la faim). Ce gisement de données avait naturellement fourni de la matière première à une plateforme citoyenne mondiale (à l'image de nombreux exemples actuels et de BitCloud). Et les premiers services citoyens territorialisés sont apparus dans ce village, tout simplement. Ces citoyens avaient tant de talents, de richesses dans un environnement contraint géographiquement et budgétairement.


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Sans le savoir, cette rupture allait faire émerger une nouvelle forme d'intelligence, l'intelligence collective holomidale, reliant des citoyens entre eux, sans structure de commandement, horizontalement, et en grand nombre. Au début, nous étions maladroits et pourtant de nombreuses richesses avaient déjà émergé (lire cet article de Valérie Peugeot sur Les collectifs numériques, source d'imaginaire politique) : partage de savoirs, monnaies alternatives, réhabilitation urbaine, solidarités intergénérationnelles ou de quartier, énergies alternatives, financements participatifs, gestion de conflits, troc de semences, circuits courts de consommation, recyclage, do-it-yourself, carte contributive....



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Future of Engagement 8: Collective Intelligence I #fastdata

Future of Engagement 8: Collective Intelligence I #fastdata | e-Xploration | Scoop.it
Organizations synthesize search, social and sensor data streams into insights that guide smarter actions. What is Collective Intelligence? .

Via Spaceweaver
luiy's insight:

"Finally, we expect that “fast data” will be the next big thing after “big data”, as organizations seek to analyze data streams from social conversations, search queries, sensors, and transactions, find patterns and actionable insights, and share it back with users to help them make better decisions, all in real time. "

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Jean-Michel Livowsky's curator insight, December 31, 2013 6:55 AM

C'est quoi finalement ce Collectif de l'intelligence ?

Saberes Sin Fronteras OVS's curator insight, January 7, 2014 1:05 PM
La inteligencia colectiva es sabido que es la base de la superviencia de las hormigas, abejas y otras especies Los seres humanos hemos elaborado a lo largo de milenios saberes y conocimientos sin los que hoy ni podriamos vivir, pero las nuevas tecnologias y las REDES abren tantas o más posibilidades para el futuro que lo que en su dia fue la escritura.
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Google Online Takedown Requests Browser I #dataviz #bigdata

Google Online Takedown Requests Browser I #dataviz #bigdata | e-Xploration | Scoop.it
An interactive visual Google Online Takedown Requests browser by Frontwise!
luiy's insight:
ABOUT THIS BROWSER

This browser visualizes trends and patterns in Google online takedown requests from copyright owners and governments. It provides a monthly overview of requests and targeted domains or products, ordered by time and volume. Colors indicate if requests were justified, and the degree of copyright infringement of the targets. Extended information about requests and targets can be revealed by hovering or clicking items

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The Emerging Science of Human-Data Interaction | #bigdata #HDI

The Emerging Science of Human-Data Interaction | #bigdata #HDI | e-Xploration | Scoop.it
The rapidly evolving ecosystems associated with personal data is creating an entirely new field of scientific study, say computer scientists. And this requires a much more powerful ethics-based infrastructure.
luiy's insight:

... Richard Mortier at the University of Nottingham in the UK and a few pals say the increasingly complex, invasive and opaque use of data should be a call to arms to change the way we study data, interact with it and control its use. Today, they publish a manifesto describing how a new science of human-data interaction is emerging from this “data ecosystem” and say that it combines disciplines such as computer science, statistics, sociology, psychology and behavioural economics.

 

They start by pointing out that the long-standing discipline of human-computer interaction research has always focused on computers as devices to be interacted with. But our interaction with the cyber world has become more sophisticated as computing power has become ubiquitous, a phenomenon driven by the Internet but also through mobile devices such as smartphones. Consequently, humans are constantly producing and revealing data in all kinds of different ways.

 

Mortier and co say there is an important distinction between data that is consciously created and released such as a Facebook profile; observed data such as online shopping behaviour; and inferred data that is created by other organisations about us, such as preferences based on friends’ preferences.


Original Article : http://arxiv.org/abs/1412.6159

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The Socialist Origins of #BigData | #Cybersyn #Cybernetics

The Socialist Origins of #BigData | #Cybersyn #Cybernetics | e-Xploration | Scoop.it
Evgeny Morozov on how the ideas behind Project Cybersyn, a futuristic experiment in cybernetics from nineteen-seventies Chile, still shapes technology.
luiy's insight:

The consultant, Stafford Beer, had been brought in by Chile’s top planners to help guide the country down what Salvador Allende, its democratically elected Marxist leader, was calling “the Chilean road to socialism.” Beer was a leading theorist of cybernetics—a discipline born of midcentury efforts to understand the role of communication in controlling social, biological, and technical systems. Chile’s government had a lot to control: Allende, who took office in November of 1970, had swiftly nationalized the country’s key industries, and he promised “worker participation” in the planning process. Beer’s mission was to deliver a hypermodern information system that would make this possible, and so bring socialism into the computer age. The system he devised had a gleaming, sci-fi name: Project Cybersyn.

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#BIGDATA SOCIETY: Age of Reputation or Age of Discrimination? | #controverses #privacy

#BIGDATA SOCIETY: Age of Reputation or Age of Discrimination? | #controverses #privacy | e-Xploration | Scoop.it
luiy's insight:

Like every technology, Big Data has some side effects. Even if you are not concerned about losing your privacy, you should be worried about one thing: discrimination. A typical application of Big Data is to distinguish different kinds of people: terrorists from normal people, good from bad insurance risks, honest tax payers from those who don't declare all income ... You may ask, isn't that a good thing? Maybe on average it is, but what if you are wrongly classified? Have you checked the information collected by the Internet about your name or gone through the list of pictures google stores about you? Even more scary than how much is known about you is the fact that there is quite some information in between which does not fit. So, what if you are stopped by border control, just because you have a similar name as a criminal suspect? If so, you might have been traumatized for quite some time.

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Chorus Project : #Twitter #analytics tool suite | #bigdata

Chorus Project : #Twitter #analytics tool suite | #bigdata | e-Xploration | Scoop.it
Twitter data retrieval and visual analytics. Designed for social research. GUI based for easy access and fast productivity.
luiy's insight:

The Chorus package currently comprises of two distinct programs:

Tweetcatcher

Firstly, we have Chorus-TCD (TweetCatcher Desktop). Tweetcatcher allows users to sift Twitter for relevant data in two distinct ways: either by topical keywords appearing in Twitter conversation widely (i.e. semantically-driven data) or by identifying a network of Twitter users and following their daily ‘Twitter lives’ (i.e. user-driven data).

Tweetvis

Secondly, we have Chorus-TV (TweetVis), which is a visual analytic suite for facilitating both quantitative and qualitative approaches to social media data in social science. Visual analytics (VA) is an interdisciplinary computing methodology combining methods from data mining, information visualization, human-computer interaction and cognitive psychology. The VA approach is highly relevant to the aims of Chorus, enabling exploratory analysis of social media data in an intuitive and user-friendly fashion. Two main views are available within Chorus-TV. The Timeline Explorer (below) provides users an opportunity to analyse Twitter data across time and visualize the unfolding Twitter conversation according to various metrics (including tweet frequency, sentiment, semantic novelty and homogeneity, collocated words, and so on).

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#Privacy, Anonymity, and #BigData in the Social Sciences | #dh #MOOC

#Privacy, Anonymity, and #BigData in the Social Sciences | #dh #MOOC | e-Xploration | Scoop.it
A recent article suggests that open science may be irreconcilable with anonymous data, requiring a reconsideration of how we protect privacy in educational data.
luiy's insight:

The short version: many people have called for making science more open and transparent by sharing data and posting data openly. This allows researchers to check each other's work and to aggregate smaller datasets into larger ones. One saying that I'm fond of is: "the best use of your dataset is something that someone else will come up with." The problem is that increasingly, all of this data is about us. In education, it's about our demographics, our learning behavior, and our performance. Across the social sciences, it's about our health, our beliefs, and our social connections. Sharing and merging data adds to the risk of disclosing those data. 

 

The article shares a case study of our efforts to strike a balance between anonymity and open science by de-identifying a dataset of learner data from HarvardX and releasing it to the public. In order to de-identify the data to a standard that we thought was reasonably resistant to reidentification efforts, we had to delete some records and blur some variables. If a learner's combination of identifying variables was too unique, we either deleted the record or scrubbed the data to make it look less unique. The result was suitable for release (in our view), but as we looked more closely at the released dataset, it wasn't suitable for science. We scrubbed the data to the point where it was problematically dissimilar from the original dataset. If you do research using our data, you can't be sure if your findings are legitimate or an artifact of de-identification. 

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What Data #Journalists Need to Do Differently | #ddj #bigdata

What Data #Journalists Need to Do Differently | #ddj #bigdata | e-Xploration | Scoop.it
Relying too heavily on the same sources leaves important stories untold.
luiy's insight:

The role of the data journalist has increased dramatically over the last decade.The past few months have seen the launch of several high-profile “data journalism” or “explanatory journalism” websites in the U.S. and the UK – such as Nate Silver’s recently relaunched and somewhat controversialFiveThirtyEight; Trinity Mirror’s ampp3d, a mobile-first site that publishes snappy viral infographics;The Upshot from The New York Times, which aims to put news into context with data; and Vox, where former Washington Post blogger Ezra Klein leads a team that provides “crucial contextual information” around news. The debates (pro and con) around these projects have brought data journalism out of its niche in digital media conferences and trade publications into the limelight.

 

These new media outlets have been received with both praise and criticism. Guardian journalist James Ball, who has been closely associated with the use of data for journalism – from his work with Wikileaks to the “Offshore Leaks” investigations – recently offered an interesting analysis of these developments. He points out a number of limitations in many of these data journalism projects — from the lack of transparency about their data, to the perpetuation of gender inequality among media professionals (“still a lot of white guys”), to the conspicuous absence of one of journalism’s most essential functions: the breaking of news.

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What Will Happen to ‘#BigData’ In Education? | #learning #analytics

What Will Happen to ‘#BigData’ In Education? | #learning #analytics | e-Xploration | Scoop.it
Privacy concerns have put the breaks on many efforts to use "big data" in education. Why are people so skittish of education data when other kinds of digital information are readily accessible?

Via Claude Emond, Pierre Levy
luiy's insight:

InBloom’s trajectory has shined a spotlight on the public’s sensitivity around what happens to student data. When it first began as a mammoth ed-tech project in 2011 by the Council of Chief State School Officers, the Bill and Melinda Gates Foundation and the Carnegie Corporation called the Shared Learning Infrastructure, the purpose was to provide open-source software to safely organize, pool, and store student data from multiple states and multiple sources in the cloud. That included everything from demographics to attendance to discipline to grades to the detailed, moment-by-moment, data produced by learning analytics programs like Dreambox and Khan Academy. An API — application programming interface — would allow software developers to connect to that data, creating applications that could, at least in theory, be used by any school in the infrastructure.

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Thoughts on #SNA and online #learning | #intelligencecollective

Thoughts on #SNA and online #learning | #intelligencecollective | e-Xploration | Scoop.it
Following the previous post... The structural paradigm of  Social Network Analysis (SNA) with its constitutive theory and methods, began to emerge around the 1930s, applied and influenced by a broa...

Via Dr. Susan Bainbridge, Marinella De Simone
luiy's insight:

The connections within nodes in a network facilitate exchange of “resources”  which can be influenced by the quantity and quality of the linkages and interactions. Looking at online educational networks through a SNA lens is a way to establish wether the ways in which individuals connect with a particular environment may influence their access to information and knowledge. As Rita Kop states “the Web is portrayed as a democratic network on which peer to peer interaction might lead to a creative explosion and participative culture of activity” (Kop, 2012 p3) but how is this potential being exploited in education? What are the processes beyond this interaction and how can they be used to facilitate students access to information, knowledge and ideas?

 

The potential of social media in forming networks, extending students knowledge and translating this into academic achievement is impacted by a multitude of elements such as individuals’ attitudes (Morrison, 2002), University environment and socialisation processes (Yu et al., 2010). Other mechanisms influencing this process may be the particular educational practices and experiences, the success of connections, the dynamics in which participants negotiate the structure of the network and exchange practices and many others which can not be controlled.

 

This analysis can be enriched by Bordieau’s concept of “social capital”, which introduces a set of dynamics between the social dimension, the identity dimension (habitus) and the individual’s practice. In this system of reciprocal influences it is interesting to look at the transformation processes and effects of elements such as “weak ties”, “brokers”, “latent connections” and “structural holes” in the information flow within a network.

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Milena Bobeva's curator insight, March 1, 2014 4:10 AM

Social Network Analysis should be a  paradigm for researching, designing, and evaluating not only online learning, but  the wider phenomenon of Education 3.0

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#BigData 's Dangerous New Era of Discrimination | #segmentation

#BigData 's Dangerous New Era of Discrimination | #segmentation | e-Xploration | Scoop.it
Where does value-added segmentation end and harmful discrimination begin?
luiy's insight:

Big Data creates Big Dilemmas. Greater knowledge of customers creates new potential and power to discriminate. Big Data — and its associated analytics — dramatically increase both the dimensionality and degrees of freedom for detailed discrimination. So where, in your corporate culture and strategy, does value-added personalization and segmentation end and harmful discrimination begin?

Let’s say, for example, that your segmentation data tells you the following:

 

Your most profitable customers by far are single women between the ages of 34 and 55 closely followed by “happily married” women with at least one child. Divorced women are slightly more profitable than “never marrieds.” Gay males — single and in relationships — are also disproportionately profitable. The “sweet spot” is urban and 28 to 50. These segments collectively account for roughly two-thirds of your profitability.  (Unexpected factoid: Your most profitable customers are overwhelmingly Amazon Prime subscriber. What might that mean?)

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Data Everywhere: #DataAnthropology, Quantified Self, Machine Data, Human Centered Design, and more | #bigdata #opendata

Data Everywhere: #DataAnthropology, Quantified Self, Machine Data, Human Centered Design, and more | #bigdata #opendata | e-Xploration | Scoop.it
In mid-February, Strata returns to Santa Clara for its fourth year. Since the conference started, it’s grown in size and scope, broadening its focus to include design...

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Using #NodeXL to decipher #bigdata | #SNA

Using #NodeXL to decipher #bigdata | #SNA | e-Xploration | Scoop.it
Quirks Marketing Research Review magazine, January 2014. Featuring articles on online research - among others.
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#FreeBook : The Field Guide to #DataScience I #bigdata

#FreeBook : The Field Guide to #DataScience I #bigdata | e-Xploration | Scoop.it
Data Science is the competitive advantage of the future for organizations interested in turning their data into a product through analytics. Industries from he…

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luiy's insight:

Booz Allen Hamilton created The Field Guide to Data Science to help organizations of all types and missions understand how to make use of data as a resource. The text spells out what Data Science is and why it matters to organizations as well as how to create Data Science teams. Along the way, our team of experts provides field-tested approaches, personal tips and tricks, and real-life case studies. Senior leaders will walk away with a deeper understanding of the concepts at the heart of Data Science. Practitioners will add to their toolboxes.

In The Field Guide to Data Science, our Booz Allen experts provide their

insights in the following areas:

 

Start Here for the Basics provides an introduction to Data Science, including what makes Data Science unique from other analysis approaches. We will help you understand Data Science maturity within an organization and how to create a robust Data Science capability. Take Off the Training Wheels is the practitioners guide to Data Science. We share our established processes, including our approach to decomposing complex Data Science problems, the Fractal Analytic Model. We conclude with the Guide to Analytic Selection to help you select the right analytic techniques to conquer your toughest challenges. Life in the Trenches gives a first hand account of life as a Data Scientist. We share insights on a variety of Data Science topics through illustrative case studies. We provide tips and tricks from our own experiences on these real-life analytic challenges. Putting it All Together highlights our successes creating Data Science solutions for our clients. It follows several projects from data to insights and see the impact Data Science can have on your organization.

 

http://www.boozallen.com/media/file/The-Field-Guide-to-Data-Science.pdf

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