Beautiful web-based timeline software Big Data I : des données à vitesse grand V Dans quel domaine la vitesse liée au Big Data se manifeste-t-elle le plus aujourd’hui ? Les analystes de Big Data font des projections à partir desquelles des êtres humains, mais aussi des machines, peuvent prendre des décisions. Et cela nous permet d’accélérer le processus de décision de telle sorte que sur le marché du comptant à Wall Street, des milliards de décisions peuvent être prises en l’espace d’une seconde. Quelle expérience en rapport avec l’accélération des données vous a le plus impressionné dans le cadre de vos recherches pour le livre « Big Data » ? Ce qui m’a le plus frappé, c’est la manière dont des chercheurs de l’Université de Toronto examinent les fonctions vitales des prématurés : ils collectent 1200 données par secondes en temps réel. Pourquoi la vitesse de transfert des données a-t-elle autant évolué depuis le début de l’ère informatique ? Pourquoi avons-nous tant de mal à comprendre ces vitesses et à nous les représenter ? Bien entendu.
Learn Data Science by nborwankar How sensor journalism can help us create data, improve our storytelling Data journalism, meet sensor journalism. You two should talk. What’s sensor journalism? I’ll get to that. But first, let me tell you a story about bugs — and a pair of gadgets that sat for months in a box under John Keefe’s bed. Keefe, senior editor for data news and journalism technology at WNYC in New York, said by phone that he had bought the Arduino microcontroller and Raspberry Pi with great excitement, played with them for a weekend, and then boxed them up. Keefe learned that when the soil eight inches down reaches 64 degrees for a few days, cicadas emerge to fill summer nights with their songs. A few days later, at an internal WNYC hackathon, Keefe presented the idea of building a cicada sensor. The Cicada Tracker has taken off from there: WNYC is now planning a pair of hack days: one where more than 250 volunteers will build sensors and another where New York schoolchildren will make even more cicada trackers. But that’s part of sensor journalism’s appeal: It’s cool. Our results.
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Gephi, an open source graph visualization and manipulation software Big Data bullshit Je suis particulièrement étonné par le discours actuel sur les big data ; discours selon lequel nous serions passé de la causalité à la corrélation. Je pense surtout à la thèse de Viktor Mayer-Schönberger et Kenneth Cukier, dans leur livre Big Data : une révolution qui va transformer notre façon de vivre, de travailler et penser. (voir l’excellent article de recension de Hubert Guillaud : Big Data : nouvelle étape de l’informatisation du monde.) Dans leur article paru dans Le Monde Diplomatique de Juillet 2013, les auteurs écrivent : “La manière dont la société traite l’information se trouve radicalement transformée. Tout d’abord les auteurs tendent à opposer la corrélation et la causalité en faisant porter cette opposition sur celle entre le pourquoi et le comment ; ce qui est une argumentation assez surprenante. La causalité est un des pôles de la corrélation, un cas particulier (que l’on pourrait qualifier de nécessaire et suffisant).
The Open Source Data Science Masters European Journalism Centre (EJC) Datavisualization.ch Selected Tools Coming Soon: Petabytes of Free Weather, Climate and Oceans Data UPDATE - Feb. 25, 9:45 a.m. ET: Big data applications are a key priority for Commerce Secretary Penny Pritzker, who has been pushing for Commerce Department agencies to explore ways of making more of their data available to businesses and the public. In a Feb. 24 speech in Silicon Valley, Pritzker said the Department's interest in making more weather and climate data publicly accessible also stems in part from the recognition that weather and climate-sensitive industries in the U.S. "account for roughly one-third of GDP." The National Oceanic and Atmospheric Administration gathers and distributes enough weather and climate data to support a multibillion-dollar private-sector weather industry. NOAA on Monday issued a request for information aimed at soliciting ideas from the private sector to help the federal agency free up much of the 20 terabytes of information that it gathers daily on the land, sea and air. Although a relatively obscure agency within the U.S.
A (small) introduction to Boosting – Sachin Joglekar's blog What is Boosting? Boosting is a machine learning meta-algorithm that aims to iteratively build an ensemble of weak learners, in an attempt to generate a strong overall model. Lets look at the highlighted parts one-by-one: 1. Weak Learners: A ‘weak learner’ is any ML algorithm (for regression/classification) that provides an accuracy slightly better than random guessing. 2. 3. 4. How does Boosting work? Usually, a Boosting framework for regression works as follows: (The overall model at step ‘i’ is denoted by 1. , usually predicting a common (average) value for all instances. 2. from to do: 2.a) Evaluate the shortcomings of , in terms of a value for each element in the training set. 2.b) Generate a new weak learner based on the s. 2.c) Compute the corresponding weight . 2.d) Update the current model as follows: where denotes the learning rate of the framework. 3. as the overall output. For example, Gradient Boosting computes as the gradient of a Loss function (whose expression involves target output . . and