Fast and slow visualization

@machinelearnbot

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.


Thinking Outside of the Visualization Box

@machinelearnbot

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'.


The Curse of #DataViz

@machinelearnbot

With the wide array of amazing data visualization tools out there - SAP Lumira, Qlik, Domo, Tableau - you would think that the world has moved towards a graphical understanding of reality. Yet my experience has been that when people are faced with the task of using a fancy new graph or visualization in their work, their first reaction is….to freak out. I am not implying that most people can't handle a nice bar chart or a three dimensional pie chart or even some animated multi-colored craziness. What I am saying is that most people don't know where to go from a data visualization. Are they supposed to take a screenshot and send it to their boss?


How to Spot Visualization Lies

@machinelearnbot

It used to be that we'd see a poorly made graph or a data design goof, laugh it up a bit, and then carry on. At some point though -- during this past year especially -- it grew more difficult to distinguish a visualization snafu from bias and deliberate misinformation.


Data visualization

@machinelearnbot

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.