For this month’s SWDChallenge, Cole challenged us to visualize the variance in data. The mode, the median or the mean may not be a model that represents the entire dataset accurately. Hence it is important to visualize variance.
For this makeover, I wanted to bring in some context with population. Comparing China and US is not fair game. China’s population is at least 4 times that of US.
Dot plots use dots to represent the position of value on the axis. They are useful for comparisons and are a good replacement for bars or clustered bars.
For this makeover, I used a dot plot to tell 2 stories. Which countries improved the most?Which countries have the highest and lowest DESI scores?
Heatmaps visualize data through variations in coloring. Heatmaps are self-explanatory and are a great way to show to a general view of data. Heatmap Framework Heatmaps consist of multivariate data plotted in rows and columns in a matrix-like fashion. Cell AE, for instance, is based on the relationship between cells A and E. When to…
For this week’s makeover, I wanted to use Tableau. Sketching a few ideas for the visualization, I was drawn towards a heat map. I started with a heat map of days vs. hour of the day. Exploring the data further, I found the monthly variation to be interesting.
The famous statistician John W. Tukey created a branch of data analysis called Exploratory Data Analysis. In exploratory data analysis we get familiar with data, ask questions, visualize data in a number of forms, look for relationships between the variables, look for outliers, patterns and trends in data. One way to do that is using measures…