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Add an article to Data Science roadmap about Skewness concept (#5982)

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This is a simple and useful article, which I think might be very useful for understanding the concept of skewness.
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Amirali Toori
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src/data/roadmaps/data-analyst/content/108-descriptive-analysis/102-distribution-shape/100-skewness.md
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# Skewness
Skewness is a crucial statistical concept driven by data analysis and is a significant parameter in understanding the distribution shape of a dataset. In essence, skewness provides a measure to define the extent and direction of asymmetry in data. A positive skewness indicates a distribution with an asymmetric tail extending towards more positive values, while a negative skew indicates a distribution with an asymmetric tail extending towards more negative values. For a data analyst, recognizing and analyzing skewness is essential as it can greatly influence model selection, prediction accuracy, and interpretation of results.
Skewness is a crucial statistical concept driven by data analysis and is a significant parameter in understanding the distribution shape of a dataset. In essence, skewness provides a measure to define the extent and direction of asymmetry in data. A positive skewness indicates a distribution with an asymmetric tail extending towards more positive values, while a negative skew indicates a distribution with an asymmetric tail extending towards more negative values. For a data analyst, recognizing and analyzing skewness is essential as it can greatly influence model selection, prediction accuracy, and interpretation of results.
Visit the following resources to learn more:
- [@article@Skewed Data](https://www.mathsisfun.com/data/skewness.html)