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Hey it's Andre here again with another quick note.

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Now if you want you can pause the video and read this little comic by X Casey.

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But I want to talk quickly about data visualizations.

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Now we talked about this idea of exploring our data right and we can explore our data as by looking

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at something like mean or variance or any statistical method to explore our data.

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But data visualization is another method we can use.

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Along with statistics to explore our data so we often create these data visualizations like graphs to

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derive meaning from data.

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But it's easy to make graphs that are meaningless or misleading.

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And your job as a data scientist is not just to make pretty graphs and pretty visualizations but to

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make visualizations that are actually meaningful and that are accurate.

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For example access is important.

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You need to know what the x axis and y axis is here.

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Otherwise well this graph is completely meaningless because we have no idea what it's measuring.

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This is a common mistake with beginners.

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That is we assume that a graph is always telling the truth.

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And we jump to conclusions right away from data.

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Instead we want to pause and reflect and make sure that whatever we're visualizing is accurate and testing

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it with different methods trying different visualizations.

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We don't want to jump to conclusions right away from data.

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It takes a lot to understand this data.

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So just a quick note that is just because we have data doesn't mean we have knowledge.

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All right.

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Back to work.