A quartile is the 25th, 50th, and 75th percentile of a data set. This means that if you were looking at the median for example, then the 25th percentile would be the median point in your data set (50% below and 50% above), while the 75th percentile would be where half your data falls below and half falls above this point.
The first quartile is the median, which is found by arranging the values in ascending order and then finding the value that divides them into two equal parts. The second quartile is found by arranging the values in descending order and then finding the value that divides them into two equal parts. The third quartile is found by arranging the values in ascending order and then finding the value that divides them into three equal parts.
The most common quartile is the first quartile. It is most likely that the first quartile will be a good fit for your needs. This is because this quartile has the least amount of information in it. The second and third quartiles are more likely to be too detailed for your needs, but they may also be a good fit depending on your specific goals.
The importance of data science is not limited to the number of data points that a model can handle. It is also important to know how those data points are distributed, which is why quartiles are important in data science.
Very good course for begineers.
Umesh Kumar Pandey
can explain more about level of management would help of more understanding