Hello data enthusiasts!
Today, we’ll be addressing a crucial topic that’s often overlooked in the exciting world of data science and machine learning: Bad and Good Data Visualisation. We know how critical it is to not just crunch those numbers and build models, but present our findings in the most effective way possible.
You know, they say a picture is worth a thousand words. This couldn’t be truer in our field! The right plot or graphs can be the difference between giving others a clear insight into the data or leaving them utterly confused. So, let’s dive into the common pitfalls we find in data visualisations and how to correct them.
The full implementation is available below:
Contents
Setup
Pitfall 1: Inappropriate Use of Pie charts
Pitfall 2: Misleading Y-axis
Pitfall 3: Overcomplication of Plot Designs
Pitfall 4: Ignoring Scale Differences
Pitfall 5: Unconsidered use of 3D Plots
Pitfall 6: Overplotting
Pitfall 7: Not Emphasizing on What’s Important
Pitfall 8: Improper use of Line charts…
