Exploratory data analysis (EDA) is a method of analyzing and understanding data sets to gain insights and identify patterns. It is an important step in the data science process and involves visualizing and summarizing data to uncover trends and relationships.
One example of EDA is using histograms to understand the distribution of a continuous variable. A histogram is a graph that displays the frequency of data within a range of values, called bins. By plotting a histogram, we can see the shape of the distribution, identify any outliers, and understand the spread of the data. For instance, if we have a dataset of heights of students in a class, we can plot a histogram to see the distribution of heights. This can help us understand if the data is normally distributed or skewed, and identify any students who are significantly taller or shorter than the average height.
Another example of EDA is using scatter plots to understand the relationship between two variables. A scatter plot is a graph that displays the relationship between two numeric variables by plotting their values on a two-dimensional coordinate system. By plotting a scatter plot, we can see if there is any linear or nonlinear relationship between the variables, and identify any outliers or clusters. For instance, if we have a dataset of test scores and study hours, we can plot a scatter plot to see if there is a relationship between the two variables. This can help us understand if studying more leads to higher test scores, or if there are other factors at play.
Overall, EDA is a crucial step in the data science process as it helps us understand and summarize data, uncover trends and relationships, and identify potential insights. By using visualizations and summaries, EDA allows us to explore and analyze data in a more effective and efficient way, leading to better decision making and predictions.