![]() The second reason is that while most persons prefer Chart 2, only a few can tell all the reasons why they like it.I have spent more than 50 hours working into my submission, and it had only 17 charts - This is about three hours of work per chart. ![]() The first reason is that creating useful graphs requires planning, time, and a lot of work.Now, why most people keep creating and delivering visualisations like Chart 1? Most of you will agree that the Chart 2 is better and does not require any explanation. Can you tell which is the good one?Ĭhart 2: Another chart exposing the same data. Let's see two charts that expose the same data. If it requires explanation, then it is not a good chart. My rule of thumb to identifying a good chart is: ![]() Simply because they are not well designed and built. And it is often the case that the charts fail to deliver the value and insights they were supposed to. Many people have to create data visualisations at work every day, from data and BI analysts to data scientists, designers, and journalists. ![]() Over the last three years, I won a total of $19,000 in prizes from those competitions. I finished in third place in 20 and got the first prize in 2020 out of more than 300 different competitors. I'm a data engineer, and my daily activities rarely include having to plot charts or conduct any kind of analysis.īut even without having lots of experience, I managed to win data visualisation competitions promoted by Kaggle for three years in a row. Okay, let's start with the truth about my background: I’m not a designer, neither a data journalist nor a data scientist. Beautiful graph builder how to#Learn how to take your data visualisation skills to the next level. ![]()
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