Developing Data Products. Natural Language Processing. Statistics. Tree mapping. Text analysis - vis. Kaggle: The Home of Data Science. Welcome to the Future | D-Wave Systems. 30 start-up qui montent dans le big data. Improving Decisions About Health, Wealth, and Happiness. DBpédia en français.
Data Analysts: What You'll Make and Where You'll Make It - Udacity - Be in Demand. The Americas. The Wild West. Outer space. Big Data. Data is the new frontier of the 21st century, ripe for exploration. You’ll be happy to know that all the buzz around data science isn’t a bunch of empty hype. Companies are buckling under a deluge of information newly available to them in an incredibly rich variety.
As Harvard Professor Gary King told Harvard Magazine, “There is a big data revolution. That’s where you come in. High Demand for the Highly Skilled Data analysts are in extremely high demand, but the work itself is equally demanding. There’s an incredible potential, already being fulfilled in industries as varied as medicine and manufacturing, to capitalize on the deep data pool available to change the course of how products are made and marketed, how processes are executed and optimized, and how society at large advances.
If you’ve got what it takes, there are plenty of companies eager to take what you’ve got. It’s not just the skills needed, it’s also the raw manpower. Outcomes, Impacts, and Indicators. The Impact Survey was first used in 2009 to help gather data for the Opportunity for All study reports, conducted by the University of Washington’s iSchool with assistance from the Bill & Melinda Gates Foundation. Libraries were enlisted to connect to a web survey, the results of which were used to augment responses gathered through a telephone-based poll. To our surprise and delight, we gathered more than 45,000 survey responses in just ten weeks, with about 400 libraries participating. Even more delightful was finding that libraries were using the data from Opportunity for All as well as the reports of Impact Survey results from their own communities.
Now, six years and several versions later, the Impact Survey is still showing the value of public access technology in libraries, and more libraries than ever are taking advantage of having outcomes, impacts, and indicators ready to measure with just a little bit of copying and pasting. Demonstrating what we do The language of evaluation. Watson (intelligence artificielle) Un prototype initial de Watson en 2011. Watson est un programme informatique d'intelligence artificielle conçu par la société IBM dans le but de répondre à des questions formulées en langage naturel[1].
Il s'intègre dans un programme de développement plus vaste, le DeepQA research project. Le nom « Watson » fait référence à Thomas J. Watson, dirigeant d'IBM de 1914 à 1956[2], avant même que cette société ne s'appelle ainsi. En 2011, Watson connaît une notoriété au niveau mondial quand il devient le champion du jeu télévisé américain Jeopardy! , en battant les meilleurs concurrents humains de l'histoire de ce jeu[3]. Quatorze ans après la confrontation entre Deep Blue et le champion d'échecs Garry Kasparov, qui avait vu la défaite de ce dernier[4], les équipes d'IBM font participer Watson au célèbre jeu télévisé américain Jeopardy!
Lors d'une session de répétition en condition de jeu réelle tenue mi-janvier 2011, Watson avait déjà gagné face aux deux champions[6]. Portail de l’informatique. ROSS—Lawyers. Tulipposy.labri. Masters of Networks 2: what we will do - Insite. Masters of Networks is essentially a hackathon. There will be no talks except a very short introduction by me. While hackathons typically organize themselves given good wi-fi and enough caffeine, we thought we would give it a modicum of structure. It works like this: There will be two teams. Team 1 – Algorithmic detection of specialization in online conversations What we do: we prototype a method for detecting emergent groups of “citizen specialists” in online consultations; people that bootstrap each other into a sort of informal high-level working group.This is relevant because: emergent specialization is likely to increase the firepower of the citizens collective intelligence in online consultation. Team 2 – Patterns in research funding in Italy How it works You show up at 10.00.
What if I am not allocated to any team? We build teams just to save time. Do you guys have a hashtag? Sure! Sounds awesome! We still have one or two places. Masters of Networks 2: Algorithmic detection of specialization in online conversations - Insite. This is a writeup of the Team 1 hackathon at Masters of Networks 2. Participants were: Benjamin Renoust, Khatuna Sandroshvili, Luca Mearelli, Federico Bo, Gaia Marcus, Kei Kreutler, Jonne Catshoek and myself. I promise you it was great fun! The goal We would like to learn whether groups of users in Edgeryders are self-organizing in specialized conversations, in which (a) people gravitate towards one or two topics, rather than spreading their participation effort across all topics, and (b) the people that gravitate towards a certain topic also gravitate towards each other.
Why is this relevant? Understanding social network dynamics and learning to see the pattern of their infrastructure can become a useful tool for policy makers to rethink the way policies are developed and implemented. Furthermore, it could ensure that policies reflect both needs and possible solutions put forward by people themselves. The data The conversation was hosted on a Drupal 6 platform. What we did 1. 2. 3. 4. 5.