What is data science

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Regardless of what you might think of the ubiquity of the "Big Data" meme, it's clear that the growing size of datasets is changing the way we approach the world around us. This is true in fields from industry to government to media to academia and virtually everywhere in between. Our increasing abilities to gather, process, visualize, and learn from large datasets is helping to push the boundaries of our knowledge. But where scientific research is concerned, this recently accelerated shift to data-centric science has a dark side, which boils down to this: the skills required to be a successful scientific researcher are increasingly indistinguishable from the skills required to be successful in industry. While academia, with typical inertia, gradually shifts to accommodate this, the rest of the world has already begun to embrace and reward these skills to a much greater degree. The Big Data Brain Drain: Why Science is in Trouble The Big Data Brain Drain: Why Science is in Trouble
La lecture de la semaine provient de la vénérable revue The Atlantic et on la doit à Erik Brynjolfsson, économiste à la Sloan School of Management et responsable du groupe Productivité numérique au Centre sur le Business numérique du Massachusetts Institute of Technology et Andrew McAfee auteurs Race Against the Machine (“La course contre les machines où comment la révolution numérique accélère l’innovation, conduit la productivité et irréversiblement transforme l’emploi et l’économie”). Elle s’intitule : “l’histoire de l’innovation contemporaine, c’est les Big Data” (c’est le nom que l’on donne à l’amoncellement des données). En 1670, commence l’article, à Delphes, en Hollande, un scientifique du nom de Anton van Leeuwenhoek (Wikipédia) fit une chose que beaucoup de scientifiques faisaient depuis 100 ans. Il construisit un microscope. L’histoire de l’innovation contemporaine c’est les Big Data L’histoire de l’innovation contemporaine c’est les Big Data
The Human Face of Big Data
Big Data, Big Hype: Big Deal
‘Big data’ is dead. What’s next? ‘Big data’ is dead. What’s next? This is a guest post by technology executive John De Goes “Big data” is dead. Vendors killed it. Well, industry leaders helped, and the media got the ball rolling, but vendors hold the most responsibility for the painful, lingering death of one of the most overhyped and poorly understood terms since the phrase “cloud computing.” Any established vendor offering a storage or analytics product for a tiny or a large amount of data is now branded as big data, even if their technology is exactly the same as it was 5 years ago (thank you, marketing departments!). Startups, too, lay claim to the moniker of “big data app” or “big data startup,” eager to soak up some of the big data money floating around in big data-focused VC funds.
Big data is dead, long live big data: Thoughts heading to Strata A recent VentureBeat article argues that “Big Data” is dead . It’s been killed by marketers. That’s an understandable frustration (and a little ironic to read about it in that particular venue). As I said sarcastically the other day, “Put your Big Data in the Cloud with a Hadoop.” You don’t have to read much industry news to get the sense that “big data” is sliding into the trough of Gartner’s hype curve. That’s natural. Big data is dead, long live big data: Thoughts heading to Strata
Research paper: What big data can do for the cultural sector « Cross Innovation Research paper: What big data can do for the cultural sector « Cross Innovation These days, data sizes are almost infinite, and organizations learn a lot about their position and succesful strategies by simply analyzing data. However, most cultural industries have not yet implemented this concept. Anthony Lilley and Paul Moore give their views on how data can benefit creative industries. This paper argues the value of big data analysis for creative institutions, but also that most of them are not even taking online data into account. The paper is a collaboration between Paul Moore, professor at the University of Ulster and researcher on (theory and practice of) the creative industries, and Anthony Lilley, media practitioner and creative concept developer with an international experience in the creative industries.
Dr. Brian Lowe, SUNY Oneonta – Analyzing "Big Data" | WAMC Dr. Brian Lowe, SUNY Oneonta – Analyzing "Big Data" | WAMC In today’s Academic Minute, Dr. Brian Lowe of the State University of New York Oneonta explains why "Big Data" is becoming a focus of academic inquiry. Dr. Brian Lowe, SUNY Oneonta – Analyzing Big Data
Why you should never trust a data visualisation | News Why you should never trust a data visualisation | News First of all, let me be clear: the headline of this article is a reference to Pete Warden's post, and should be read in the same way - as a caution against blind acceptance, rather than the wholesale condemnation of data visualisation. An excellent blogpost has been receiving a lot of attention over the last week. Pete Warden, an experienced data scientist and author for O'Reilly on all things data, writes: The wonderful thing about being a data scientist is that I get all of the credibility of genuine science, with none of the irritating peer review or reproducibility worries ... I thought I was publishing an entertaining view of some data I'd extracted, but it was treated like a scientific study. This is an important acknowledgement of a very real problem, but in my view Warden has the wrong target in his crosshairs.
Ethique Big Data De Ethique Big Data. Charte Ethique & Big Data Quelques Définitions La Charte en PDF Version française Ethique Big Data
Le Big Data : c’est de « la connerie » Directeur technologique de la campagne 2012 de Barack Obama, Harper Reed a son mot à dire sur le thème du Big Data. Et ce ne sont pas des éloges. En tout cas en ce qui concerne l’utilisation de ce terme par l’industrie IT. « Le problème avec le big data c’est le ‘big’, qui est une connerie. Cet aspect est déjà résolu » estime ainsi Harper Reed, qui s’exprimait à l’occasion du CeBit Australie. « Nous avons traité le big et devrions nous préoccuper de la donnée. Le Big Data : c’est de « la connerie »
http://www.alliancebigdata.com
Par Hubert Guillaud le 03/05/13 | 6 commentaires | 4,510 lectures | Impression L’analyse des grandes quantités de données – le Big Data – est appelée à révolutionner bien des domaines. L’emploi et les ressources humaines pourraient même devenir l’un de ses premiers terrains d’application. Bien sûr, rappelle Steve Lohr dans Bits, le blog techno du New York Times, “la science de la force de travail” – comme on commence à l’appeler – n’est pas nouvelle. Le management “scientifique” et la mesure statistique de l’efficacité du travail ou du recrutement ont déjà connu bien des méthodes… (et pas que des succès) : “Ce qui est différent aujourd’hui”, explique Mitchell Hoffman, économiste et chercheur à l’école de Management de Yale, “est le montant et le détail des données sur les travailleurs qui sont recueillies”. L’emploi à l’épreuve des algorithmes L’emploi à l’épreuve des algorithmes
Big Data : nouvelle étape de l’informatisation du monde Viktor Mayer-Schönberger, professeur à l’Oxford internet Institute, et Kenneth Cukier, responsable des données pour The Economist ont récemment publié Big Data : une révolution qui va transformer notre façon de vivre, de travailler et penser (le site dédié). Ce livre est intéressant à plus d’un titre, mais avant tout pour ce qu’il nous apprend du changement du monde en cours. Riche d’exemples, facilement accessibles, il dresse un état compréhensible des enjeux des Big Data en insistant notamment sur ce que cette nouvelle étape de l’informatisation transforme. Le code n’est plus la loi ! “Les systèmes informatiques fondent leurs décisions sur des règles qu’ils ont été explicitement programmés à suivre.
To Hypothesize or Not to Hypothesize "Not everything that counts can be counted, and not everything that can be counted counts." - Albert Einstein . Data Science is in the early stage of development and needs to develop canons to guide us. There is a brewing debate about the use of established scientific methods in the practice of data science. Some suggest traditional scientific methods must be used while others assert new scientific methods must be developed - especially considering algorithms, machine learning and future artificial intelligence. Part of that debate includes whether it is necessary to form a hypothesis. I suggest the answer is it depends. .
There is a lot of talk about "big data" at the moment. For example, this is Big Data Week, which will see events about big data in dozens of cities around the world. But the discussions around big data miss a much bigger and more important picture: the real opportunity is not big data, but small data. Not centralized "big iron", but decentralized data wrangling. Forget big data, small data is the real revolution | News
The field has been spawned by the enormous amounts of data that modern technologies create — be it the online behavior of Facebook users, tissue samples of cancer patients, purchasing habits of grocery shoppers or crime statistics of cities. Data scientists are the magicians of the Big Data era. They crunch the data, use mathematical models to analyze it and create narratives or visualizations to explain it, then suggest how to use the information to make decisions. Universities Offer Courses in a Hot New Field - Data Science
Pour le Data Science Institute, ces nouveaux métiers que nous regroupons sous le terme de Science des Données, combinent justement trois composantes : la statistique, l'informatique et la communication. Nous avons donc tout naturellement choisi de nos associer à cette année internationale de la statistique afin de promouvoir ces savoirs et les métiers qui s'y rattachent. Nous ne sommes pas seuls ! Loin s'en faut, puisque plus de 1400 organisations, en provenance de 108 pays, ont déjà rejoint cette initiative. Universités, écoles, centres de formation, organisations publiques et même des éditeurs de logiciels se mobilisent en 2013. L'Institut pour la Science des Données participe à l'année internationale de la statistique
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Competitions
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So you want to be a data scientist? : Nature Jobs Blog
Big Data

Big Data

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(9) What is data science