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Balaji Prabhakar Home. Netexplo.org. Smart Cities et Data Science – Partie 2 – le blog. Par Emmanuel Manceau et Alberto Guggiola Chez Quantmetry, nous avons décidé d’y voir plus clair sur le sujet Smart Cities, très à la mode depuis quelques années déjà.

Smart Cities et Data Science – Partie 2 – le blog

En particulier, nous avons décidé de considérer une dizaine de cas d’usage spécifiques, de les détailler et d’en évaluer l’intérêt pour une entreprise comme la nôtre, spécialiste dans le domaine de la Data Science. Dans la première partie de ce travail, sortie il y a quelques semaines sur notre blog, des cas d’utilisation tels que la réalisation d’un système d’éclairage adaptatif, d’une offre de stationnement dynamique ou encore d’une plateforme de marketing digital touristique ont été analysés. 6. L’introduction de capteurs dans le réseau électrique et chez les compteurs des usagers permet une compréhension plus fine des usages. En termes de Data Science, nous trouvons deux sujets à traiter. Big Data - Les nouveaux devins - Documentaire. Vous êtes cinéphile ? Voici une formation Big Data pour vous ! – After the Web. Openvalue.co.

Partnerships

Qlik. Profession "Chief Data Officer" PARTNERSHIP — TheFamily. 5 Books Every Data Professional Needs. You’ll find a large selection of books available on the topic of data analytics and business intelligence, but how do you choose the ones that will best support your career?

5 Books Every Data Professional Needs

Capella University faculty members provide their top five recommended books that every data professional should read. 1. Data governance. Data governance is an emerging discipline with an evolving definition.

Data governance

The discipline embodies a convergence of data quality, data management, data policies, business process management, and risk management surrounding the handling of data in an organization. Through data governance, organizations are looking to exercise positive control over the processes and methods used by their data stewards and data custodians to handle data. Analytics team structure can work without data scientists. When executives at medical benefits management company eviCore Healthcare started thinking about how to take better advantage of real-time and predictive analytics in 2013, they wanted to avoid building an analytics team structure that would stand separate from eviCore's business units.

Analytics team structure can work without data scientists

In particular, Matt Cunningham, an executive vice president who is in charge of operational improvement initiatives, knew from his prior experience as head of IT that building an IT-centric analytics infrastructure can lead to data silos that make it hard to share meaningful information. His goal was to "hand the business back to the business" through the increased analytics efforts. "We should be able to drive the analytics and decision making to the lowest level possible," Cunningham said. For that reason, eviCore eschewed much of what's often associated with advanced analytics programs.

Cunningham hired a team of four analysts, all of whom have master's degrees rather than Ph.Ds. GAFAnomics Saison 2 : 4 supers-pouvoirs pour exceller dans la network economy. In the new study GAFAnomics, FABERNOVEL identifies 4 superpowers to outperform in the Network Economy.

GAFAnomics Saison 2 : 4 supers-pouvoirs pour exceller dans la network economy

Understand how to become a ” Magnetic”, ” Real Time”, “Intimate” or ” Infinite ” company: 4 economic superpowers inspired by the GAFA model. FABERNOVEL released a new study analyzing the strategic practices of the GAFA companies. The study, which presents actionable lessons for legacy industries looking to reshape their strategy for the New Economy, finds that the fastest-growing superpowers in the Network Economy position themselves as Magnet, Intimate, Real Time, or Infinite Enterprises.

FABERNOVEL’s 2014 report, GAFAnomics: New Economy, New Rules detailed how Google, Apple, Facebook, and Amazon – GAFA – are driven by a common vision of a borderless market and a customer culture which redefined their notions of value creation, core business, and talent management. Read our Press Release.

Tableau

Understanding the DATEPARSE Function. Beginning with Tableau Desktop 8.1, you can use the DATEPARSE function in a calculated field to convert a date string into a datetime data type.

Understanding the DATEPARSE Function

This is useful when you are working with date data that Tableau recognizes only as a string data type. DATEPARSE function parameters The DATEPARSE function is available for non-legacy Microsoft Excel and text file connections, MySQL, Oracle, PostgreSQL, and Tableau data extracts data sources. The DATEPARSE function takes two parameters, “format” and “string.” DATEPARSE (format, string) Format: The format parameter is combination of symbols that represent the format of your date string. Refer to the Date Functions topic in Desktop Help for more information. Locale considerations The DATEPARSE function relies on the locale specified by your computer settings to recognize and display the strings that you want to convert. Untitled. Magic Quadrant. The Gartner Magic Quadrant (MQ) is the brand name for a series of market research reports published by Gartner Inc., a US-based research and advisory firm.

Magic Quadrant

According to Gartner, the Magic Quadrant aims to provide a qualitative analysis into a market and its direction, maturity and participants.[1] Their analyses are conducted for several specific technology industries and are updated every 1–2 years. Rating[edit] Gartner rates vendors upon two criteria: completeness of vision[1] and ability to execute.[1] Using a methodology which Gartner does not disclose, these component scores lead to a vendor position in one of four quadrants: Leaders are said to score higher on both criteria: the ability to execute and completeness of vision. Criticism[edit] It has been pointed out that the criteria for the Magic Quadrant cater more towards investors and large vendors than towards buyers.[2] Special Reports. Amey : Rail. Logiciels et ingénierie pour simplifier la mobilité. Tuup. Members — ITxPT — Information Technology for Public Transport. Login required - ITxPT Public Wiki.

Ants, collective intelligence. Contact INRIX - INRIX. Government- Quantum Inventions. « Les sciences sociales ne jouent plus leur rôle de contre-pouvoir » Dominique Boullier, professeur de sociologie et chercheur au Médialab à Sciences Po Paris, est spécialiste du numérique.

« Les sciences sociales ne jouent plus leur rôle de contre-pouvoir »

Il est, depuis 2012, directeur exécutif du programme international Forccast, qui vise à favoriser l’émergence de projets éducatifs numériques innovants. Il participera à la table ronde sur le thème « Civilisation numérique : quels contre-pouvoirs ? », organisée dans le cadre du Monde Festival et animée par Laure Belot. Si Facebook était un pays, il serait, avec 1,4 milliard de membres, plus peuplé que la Chine. L’entreprise annonce ne plus vouloir seulement « connecter » la planète, mais aussi « comprendre le monde ». Pour analyser les données qu’elles collectent, les plates-formes de type Google, Facebook ou LinkedIn embauchent massivement des personnes aux formations diverses (communication, économie, sciences politiques et même anthropologie).

PoweredBy. Public Transportation Startups. EcoVadis: Supplier Sustainability Ratings for Sustainable Supply Chains. How to Copy a List of Files in a Windows Folder Into an Excel List.

MOOCs

Fondamentaux pour le Big Data. Par rapport à ce cours.

Fondamentaux pour le Big Data

Big Data. Portail billettique - Les développements liés à Transmodel. 6 janvier 2012 Dans cette partie sont présentés les développements normatifs basés sur la norme TRANSMODEL, ainsi que des exemples d’implémentations sur le terrain :

Portail billettique - Les développements liés à Transmodel

Quora. 2011-11_IBM_Broch_SmarterCities_FR_Fx_5.pdf. ITF Latest Publications. Expanding Airport Capacity: Competition and Connectivity. The case of Gatwick and Heathrow The Airports Commission was set up by the Government of the United Kingdom in 2012 to take an independent look at the UK’s future airport capacity needs. It has been tasked with setting out the nature, scale, and timing of steps needed to maintain the UK’s status as an international hub for aviation, alongside recommendations for making better use of the UK’s existing runway capacity by the end of 2013; and setting out recommendations on how to meet any need for additional airport capacity in the longer-term by the summer of 2015.

In December 2013 the Commission published its Interim Report, which included a short-list of three options for increasing the UK’s aviation capacity in the long-term: two at Heathrow and one at Gatwick. This report is part of the International Transport Forum’s Country-Specific Policy Analysis (CSPA) series. 89 pages; ITF, Paris, November 2014 Go to publication. Www.keolis. The emergence of a new data science hero. The explosion of big data and its opportunities for uncovering new insights is shaping the emerging data scientist role into the superhero of this generation, and possibly generations to come.

Many organizations are creating data-driven strategies to innovate, compete and capture value from massive amounts of information they collect and store. Hadoop: A quick and easy guide to this flexible framework. When diving into the topic of big data, one soon comes across an odd-sounding name: Hadoop. What exactly is Apache Hadoop? Put simply, Hadoop can be thought of as a set of programs and procedures that anyone can use as the backbone for big data operations. Hadoop is also open source, which means it is essentially complimentary for anyone to use or modify. Without oversimplifying the topic, and because a lot of people reading this post are not software engineers, here is a brief guide for anyone wanting to know a bit more about the nuts and bolts that make big data analysis possible.

Hadoop enters the picture. Big data by Thales: smart and secure. In an age of constant change and growing complexity, intelligent solutions from Thales provide reliable, context-sensitive information that help customers understand the world around them so they can make the right decisions at the right time. Transforming vast quantities of raw data into useful insights and critical information is Thales's core business. Thales and Big data: unique positioning "Wherever safety and security are critical, Thales delivers. Together, we innovate with our customers to build smarter solutions. Everywhere! " Hadoop. Un article de Wikipédia, l'encyclopédie libre.

La Collection Marketing by RATP Dev Tout comprendre des évolutions de la mobilité. Big Data: The Key Vocabulary Everyone Should Understand.