Posts by Category - Scoop

Data Visualization is not a ‘magic bullet’ solution to the need for more evidence informed decision-making. So what are the instances in which data visualization is an appropriate form of research communication? What are the ways to enhance the effectiveness of data visualization? Imogen Robinson from SciDev.Net looks for answers to these questions.

In June 6th issue of Vizually we looked at how to create a bump chart in Tableau. Can similar chart be created in Excel? Yes. Ben Collins shows us how.

How good a lexicon based sentiment analysis method, which assigns a positive or negative score to a word, works on the entire document? David Robinson performed sentiment analysis on Yelp reviews to find out this answer. His findings: It’s good, but far from perfect!

Can Wikipedia help us understand statistical concepts such as Central Limit Theorem? Sure, if you would like to know the mathematical definition and the theory. But if you want to explain central limit theorem to a 10 year-old, a much better resource is an interactive visualization by Michael Freeman. Watch how large number of sample means when plotted on a chart form a perfectly shaped bell curve!

The numbers and statistics can be used to communicate facts or hide the truth. In this post, Ben Orlin looks at how mean, mode, correlation, etc. can be used to communicate a “good story” when the underlying data tells us otherwise.

A critical component of successful interactive visualizations is orienting the users to what they are looking at. This post explains how the simple act of scrolling can be harnessed to direct a user through a complex visualization combined intuitively with a guiding narration.

The law firm Baker & Hostetler recently hired “Ross,” an artificial intelligence software package built on IBM’s Watson cognitive computing platform that is designed to perform the work of legal researchers to support attorneys.

Samson Hu from 500px walks us through the process of building data warehouse and reporting infrastructure.

How do we read pie charts? Do they differ from the even more reviled donut charts? In two papers presented at EuroVis, Drew Skau and Robert Kosara show that the common wisdom about how we read these charts (by angle) is almost certainly wrong, and that things are much more complicated than we thought.

If you make charts on the internet, angry email about those charts is inevitable. Especially if your charts sometimes use a y-axis that starts at a number other than zero. You see, an old book called How to Lie With Statistics has convinced people that truncated axes are a devilish tool of deception.