Data Science Essentials: Mastering Basic Statistics for Effective Analysis
In the field of data science, statistical metrics play a crucial role in analyzing and interpreting data. These metrics provide valuable insights into the characteristics and patterns of the data, enabling data scientists to make informed decisions. This article will explore some commonly used statistical metrics and demonstrate how to calculate them using the popular Python library, Pandas and R. Mean: The mean, or average, is a fundamental statistical metric representing a dataset's central tendency. It is calculated by summing all the values in the dataset and dividing by the number of observations. In Pandas, we can compute the mean using the mean() function. import pandas as pd data = pd . Series ([ 2 , 4 , 6 , 8 , 10 ]) mean_value = data . mean () print (" Mean: ", mean_value ) To calculate the mean of a dataset in R, you can use the mean() function: data <- c ( 2 , 4 , 6 , 8 , 10 ) mean_value <- mean ( ...