There's still time to secure a place at the Data Visualization Summit, which returns to the Westin Copley Place in Boston in just 4 weeks time, on September 25 & 26. This event will bring together the leading data visualization experts for an exploration of the tools, trends and technologies that are shaping the future of this diverse discipline. In addition to keynote presentations, workshops, panels and countless networking opportunities, we've also launched our first Data Visualization Poster Competition. For a chance to showcase your skills, ideas and understanding to an executive led audience of data visualization gurus (and win great prizes), submit a visualization that presents data in a digestible way by Tuesday, September 9. For a sneak-peak of what to expect at the summit, check out a presentation from the Senior Data Scientist at Jawbone, 'Dream a Little Bigger'.
This breakneck pace is a real data visualization constraint. It's not a myth that charts are often deployed in rooms full of people who only have a short time to comprehend them (or not) and make a decision. Automatic views into datasources are a critical aspect of exploratory data analysis and health checks. The fast mode of data visualization is real and important, but when we let it become our only view into what data visualization is, we limit ourselves in planning for how to build, support and design data visualization. We limit not only data visualization creators but also data visualization readers.
I think many of these visualizations are just pure art, but delivering no insight. So we selected a few of the most powerful ones for you. Click here to check the 50 visualizations. Check out our previous article on great visualizations. Or do a DSC search to find all our articles about visualization.
Data visualization is what attracts at first sight is what properly should help affirm what text and numerical results say (Anscombe's quartet). It is important because otherwise it may bring confusion. For some time I was looking for information on the subject and I must say it is abundant. There are resources that can be used to improve the graphical representation of our investigation as we can find organized on Nature Methods and in the pages of the courses CS 171 - Visualization and CS 1109 Data Science on the subject of the prestigious Harvard University. The tools to perform graphics are abundant most important and most used are python and R also can be used more easily Matlab or Tableau, BoxPlotR,etc.