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When doing these projects, try to cultivate the habit of being extremely careful in assessing your visualizations.

» MORE: Data Analyst Skills: How to Excel at Data Analytics and Improve Your Skillset for the Real World Mislabeled plots, axes on the wrong scales, variables that aren’t properly formatted, and other little problems can work silently to undermine your attempts at clarity. Among the best ways to approach this is to throw up some quick charts in matplotlib or Altair and just see what’s there.Īs with statistics, however, it’s very easy to make a visualization that creates more confusion than it removes. This involves familiarizing yourself with the overall patterns and characteristics of a dataset. One of the first things a data scientist does with new data is perform ‘exploratory data analysis’, or EDA. The Importance of Data Visualization What’s important in life? Caffeine and data visualization.Īs alluded to in the introduction, data visualization is baked into data science at a really deep level.īuilding a good visual representation of data is important not just for the non-technical consumers of a data scientist’s work, but for the data scientist herself. These projects will set you on the path to learning this subtle and important craft.