Logiciels/applications. Mind Mapping, cartes heuristiques, mindmap, prezi. OUTILS - TECHNO. Data Journalisme. Le datajournalisme en 10 sites. Data Visualization with TREEMAPPER. Analysing huge datasets can be a problem sometimes.
Which keywords get more traffic and have good conversion rate compared to others? Which traffic sources get the maximum qualified visitors to your site? Which page is good at driving visitors or which engages a visitor well? You would often wish there was some visualization solution that could answer these question in one shot. My answer to this would be Treemapping! To begin with, lets understand what are Treemaps?
Data visualization DIY - Our Top Tools. Data visualization DIY: Our Top Tools Figure 102.
The Wikileaks war logs (The Guardian) What data visualization tools are out there on the web that are easy to use — and free? Here on the Datablog and Datastore we try to do as much as possible using the internet’s powerful free options. That may sound a little disingenuous, in that we obviously have access to the Guardian’s amazing graphics and interactive teams for those pieces where we have a little more time — such as this map of public spending(created using Adobe Illustrator) or this Twitter riots interactive. But for our day-to-day work, we often use tools that anyone can — and create graphics that anyone else can too. This online database and mapping tool has become our default for producing quick and detailed maps, especially those where you need to zoom in.
And these technologies are free, open and can be utilized in essentially every current web-browsing device. While it’s tempting to just jump in and add the latest cool feature to your site, you have to first make sure it’s a good fit for your users and your brand. In this 2-part series, I’m going to focus on the rapidly changing world of interactive tools for data visualization, or dataviz. These tools are especially useful for nonprofits and financial organizations whose brands rely on sharing metrics to deliver impact and value. Data journalism. Retour sur la formation Datajournalism et Traitement des données. Les vendredi 26 et samedi 27 octobre 2012, l’association Libertic accueillait à la cantine numérique nantaise et avec le soutien de Nantes Métropole , Caroline Goulard de Dataveyes afin d’initier une vingtaine de participants aux principes de datajournalism et traitement de données. Cette formation avait notamment pour objectif de répondre aux questions suivantes : Qu’est-ce que le datajournalism ?
Données fleuries, une veille hebdomadaire de datajournalisme. En vedette Je n’aurais sans doute pas parlé de cette magnifique animation publiée sur YouTube en novembre dernier – et qui a déjà atteint plus de 3,5 millions de vues – si “Mashable” n’en avait pas fait récemment un article qui a beaucoup tourné.
Wealth Inequity in America veut mettre en valeur la distribution des richesses aux Etats-Unis sous un angle particulièrement efficace : celui de nos préjugés (tiens, ça me rappelle un article sur Google Suggest qu’on avait écrit à 4 mains avec Julien Goetz sur “Owni”) et de notre perception des inégalités. En les comparant, évidemment, avec les vrais chiffres. Et comme il existe une marge très importante entre ce que les Américains pensent des inégalités dans leur pays – entre ce qu’ils croient, ce qu’ils souhaitent, et ce qui se passe vraiment – le choix du “motion design” pour illustrer ces fluctuations de données est idéal. Et, en l’occurrence, très bien réalisé. How to teach/learn data journalism: Tools vs programming/coding. The popular data tools are for every digital journalist to learn and use in everyday work, and the “high-end” programming/coding is for people who want to be a “data specialist.”
Journalism programs can incorporate basic data tools in a (required) intro digital journalism course, and teach “high-end” data journalism, in an elective course, to students who want to pursue further in this area. Understanding data journalism: Overview of resources, tools and topics. (U.S.
Census Bureau) The notion that journalism should become more data-driven — and get a little closer to social science — is not a completely new idea. The journalistic sub-field of computer-assisted reporting, embodied in the work of the Investigative Reporters and Editors’ NICAR program, has a long history. But a new suite of widely available Internet-based tools has made such a social science-journalism hybrid a real possibility for greater numbers of non-specialist journalists. Though many journalists will continue to report in traditional ways, for all media members it is worth getting familiar with some of the new tools.
Data Tools - Data Journalism Blog. 6 Great Interactive Data Visualization Tools (Part 2) Welcome back for the second part of my series on interactive data visualization (dataviz) tools.
In Part 1, we covered three cool tools for visualizing charts and graphs and many other data types on a webpage. In part two, we take a look at three more tools that are a bit more complex but have some incredible data visualization capabilities. 4. Simile Exhibit Exhibit is a very robust and customizable offering. Visualization Types Supported: Line Graphs, Maps, Scatter Plots, Multi-Filterable Lists, Timelines, Timeplots and more…with widgets!
Flexible & Powerful Approach to Design I really like the approach of Exhibit, where data is presented through a “lens” – an HTML template shell that elements are placed into. Strong Filtering/Sorting/Search Letting users filter your data by any number of criteria is incredibly useful, and turns your information from static content into a real interactive feature. Widgets! 5. 6. Wow, D3.js is cool! Junk Charts. Via Twitter, Bart S (@BartSchuijt) sent me to this TechCrunch article, which contains several uninspiring charts.
The most disturbing one is this: There is a classic Tufte class here: only five numbers and yet the chart is so confusing. And yes, they reversed the axis. Lower means higher "app abandonment" and higher means lower "app abandonment". The co-existence of the data labels, gridlines, and axis labels increases processing time without adding information. A simple column chart shows there is almost nothing going on: I suspect that if they were to break the data down by months and weeks, it would be clear that the fluctuations are meaningless.
The graphical scaffolding, or what Tufte calls the non-data ink, should provide context to help readers understand the data. Worse, the context needed to interpret "app abandonment" is sorely missing. You might argue with me. That definition is an emperor with no clothes.