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FAQs

Data science has been widely used across many industries to provide insights into customer behavior, market trends, and product performance. It's also been used to predict future outcomes. However, as the data becomes more complex, it can become difficult to interpret these insights without skewness in data science.

A skewed distribution of data is a distribution where the values are more concentrated at one end of the data set. This can happen due to various reasons, such as an outlier in the dataset or a small sample size.

The possible effects on Data Science are:

  • improvement in predictive accuracy
  • reduction in time taken
  • increase in efficiency
  • increase in knowledge discovery
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It is important to understand that data can be skewed in any number of ways. The data can be skewed by the way it was collected, how it was analyzed, or how the decision was made. A data collection method could be biased because it only includes people who are willing to answer a survey or those who have a certain set of demographics.

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