The Art of Data Visualization

Friends of Wisdom We need a revolution in the aims and methods of academic inquiry. Instead of giving priority to the search for knowledge, academia needs to devote itself to seeking and promoting wisdom by rational means, wisdom being the capacity to realize what is of value in life, for oneself and others, wisdom thus including knowledge but much else besides. A basic task ought to be to help humanity learn how to create a better world. Natural science has been extraordinarily successful in increasing knowledge. The revolution we need would change every branch and aspect of academic inquiry. These changes are not arbitrary. For more detailed presentations of the above argument see the following by Nicholas Maxwell: "Can Humanity Learn to become Civilized?

Una exploración de los datos de restaurantes en Manhattan Data analysis - Wikipedia Analysis of data is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. The process of data analysis[edit] Data science process flowchart Analysis refers to breaking a whole into its separate components for individual examination. There are several phases that can be distinguished, described below. Data requirements[edit] Data collection[edit] Data is collected from a variety of sources. Data processing[edit] Education[edit]

Convocatorias. The 2017 Information is Beautiful Awards Shortlist is now live. Now it's time for our panel of expert judges and YOU, the dataviz-loving public, to decide the winners. Before you vote for your favourites, consider what our judges look out for in a successful data visualization or infographic: Is it interesting? Plus: "Je ne sais quoi": Originality, unexpected twists, creative flair, new angle on old subject Red flags: Anything too technical, or succeeds on only one or two dimensions To see how these elements work together, hop over to Information is Beautiful founder David McCandless's What Makes a Good Visualization :: View the 2017 Longlist :: See last year's Shortlist & winners

Data analysis - Wikipedia Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. The process of data analysis[edit] Data cleaning[edit] Quality of data[edit]

Minerva is a web tool for supporting philosophical historiography research Manage Your Data: Data Management: Subject Guides The MIT Libraries supports the MIT community in the management and curation of research data by providing the following services: Data Management Guide This Data Management and Publishing Guide is a practical self-help guide to the management and curation of research data throughout its life cycle. Assistance with Creating Data Management Plans Many funders, such as the National Science Foundation, have requirements for data sharing and data management plans. Workshops Our workshops teach you how to manage data more efficiently for your own use and help you to effectively share your data with others. Individual Consultation and Collaboration with Researchers We are available for individual consultation on data management issues, and can provide expertise in areas such as data organization and preservation, connect you to a network of data management services, and advocate for your needs. Referrals to Related Services Contact Us

Ben Fry Makes Your Sweaty FuelBand Runs Look Gorgeous For all the insight available from self-quantification tools, such as the Nike FuelBand, the data its users get is fairly uniform. Activity is treated with roughly the same metric–Fuel points–and a rudimentary line graph, its peaks and valleys varying slightly for each individual wearer. So when Fathom Information Design got access to datasets for 1 million FuelBand users, the company decided to make these activity portraits as singular as the people themselves. “We were struck by how unique people are,” says Ben Fry, principal at Fathom. The layers of colors allow for an extra bit of insight: it’s easy enough to see your early morning run reflected back as a spike in activity every day, but once a shade of red starts to fill in for the frequency, you see how diligent you actually are. Fry says that this is just the beginning of such detailed self-quantification. See the Year in Fuel, here.

Knowledge to Wisdom Data, Information, Knowledge and Wisdom From SystemsWiki by Gene Bellinger, Durval Castro, Anthony Mills There is probably no segment of activity in the world attracting as much attention at present as that of knowledge management. Yet as I entered this arena of activity I quickly found there didn't seem to be a wealth of sources that seemed to make sense in terms of defining what knowledge actually was, and how was it differentiated from data, information, and wisdom. What follows is the current level of understanding I have been able to piece together regarding data, information, knowledge, and wisdom. I figured to understand one of them I had to understand all of them. According to Russell Ackoff [1989], a systems theorist and professor of organizational change, the content of the human mind can be classified into five categories: Ackoff indicates that the first four categories relate to the past; they deal with what has been or what is known. A further elaboration of Ackoff's definitions follows: Data... data is raw. Addendum