One picture is worthing a thousand words... Discover the best visuals, infographics, mind maps, concept maps and pictures as well as visualization technics such as: mind mapping, concept mapping, graphic facilitation... in order to get inspired to tell your stories.
Concept mapping and mind mapping are powerful graphic organizers, strategies for organizing and representing knowledge. They were developed during the seventies.The terms may seem to be interchangeable, but there are some differences in the way they are created and used.
Predictive analysis is a journey of refinement of data over time using a predictive model.
Emeric Nectoux's insight:
Below are five most common mistakes businesses should keep in mind while working with predictive analytics. 1. Correlation does not mean causation: Oldest rule in the book. While creating correlation matrix the most common mistake done is considering that if there exists correlation between two variables, it implies that one causes the other. Not always true. 2. Not Looking for patterns: This holds true in most of the big data analysis for ex. decision trees can grow exponentially large, so be on the lookout for patterns when you use them. If you’re taking a test and you forget some rules of combinatorics, making a simpler instance of your test question may help you find patterns more quickly. Models capture relationships among many factors(& variables) to allow assessment of risk or potential associated with a particular set of conditions, thus decision making. This also brings us to the third point, 3. Failing to consider enough variables: When deciding which variable should be used to include in a model, one should include every variable that seems predictive, most of the data models remove unnecessary variables/factors while computing. Also lack of creating and including custom variables (Using the by operator ex. Visitors by visit) is another mistake one should avoid. 4. Using Future Data to create Future Data: After generating a desired(as close as possible) result using one technique, most of the times the data is not saved or is ignored; The magic lies in saving the result and cross-computing it again with the current data or previously saved results. Ex. In Data Workbenchone can save the obtained results after analysis of a dimension with the other to create a new dimension for the future use. Not using this technique is a big miss! 5. Lack of Experimentation: Most of the times when a company invests in good analytical tool it is trying to obtain results as did another; even the data models used are ones which have proven success with a competitor. There is nothing wrong with it but there is no guarantee that a predictive model which brought 90% success rate for one company, will also get same or better for the other. One point commonly skipped while selecting models is the Data, Your company's data might be very different from the other even being a competitor, the anomaly does not lies in the type of data but in the behavior of data. The solution is to not just adapt a model but to tweak it in a way which is feasible for your data.
“La facilitation graphique met en jeu trois types de processus :la récolte d’informations,le tri et traitement de ces informations pour identifier les trames et contenus de valeurs,les traitements graphiques de ces trames et contenus pour les donner à voir.”
Via Mickael Ruau
Emeric Nectoux's insight:
La facilitation graphique est une pratique qui utilise les médias graphiques pour favoriser les échanges et les réflexions au sein d’un groupe afin de lui permettre de construire une vision commune. Elle sert, alimente et accompagne un processus de collaboration.
This visual search engine provides a view that may help learners discover additional search terms, allowing them to more easily find the information they are looking for. His post (linked to by clicking his name above) explains that this is just the beginning with additional items being added over the summer.
Providing learners access to search engines beyond Google, Bing and Yahoo is important. There are many search engines that are geared to younger students, that are more visual in nature...this list could go on. The visual component of this is colorful and allows learners to view their search in a format that many find helpful.
Data provides us with much more of a backstory that we usually realize. Maybe it’s because it takes an amazingly trained mind to harvest that data, or to create something visually compelling out of it—but we can do so much more with data than simply draw conclusions.
Emeric Nectoux's insight:
My favorite one among these 8: How Seattle Residents Determine Housing. This infographic shows at one sight what are the main criteria when it comes to housing.
Graphic recording, facilitation graphique, scribing…pour vous c’est du chinois ? Ces pratiques qui utilisent les médias graphiques pour favoriser les échanges et les réflexions au sein d’un groupe sont de plus en plus en vogue dans les entreprises, et même au-delà de leurs murs. Tour d’horizon.
...Le subjonctif présent effraie souvent les apprenants de FLE (français langue étrangère). Une carte mentale peut les aider à surmonter cette peur, et elle peut également servir d'aide-mémoire pendant les exercices...
"Engaging your mind takes some effort to identify what you care for and what is the best way to attain what you care for. Do you want students to become better learners? Help them discover what they care for by allowing them to identify and use their own learning style."
How Geospatial Data Can Enrich Your Customer Experience and Drive Revenue Smart Data Collective Geospatial data is created by a vast array of different applications such as satellites, digital cameras, wearables, smartphones, radars, sensor...
Quitte impressive... and a bit crazy. The biggest cities are the own the grows the more. Take Tokyo as an example: from 11 millions people in 1950 to 37 millions in 2030. How could it be? What is the life quality people expects?Don't we have all the means to decentralize?
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