The adage, "A picture is worth a thousand words," perfectly encapsulates the power of data visualization. For example, you have been handed an Excel sheet filled with thousands of rows of data, with columns representing various variables. Without having any prior insights about the dataset, one cannot draw any conclusion about it. Simply sifting through the rows and trying to decipher patterns or relationships hidden within does not help and one may find herself lost in the numbers. Implementing any random statistical methods might be risky as it can lead to erroneous results/conclusion about the relationships among the variables. spending hours Most statistical tests have specific assumptions about data distribution, category, etc. Here comes the strength of data visualization techniques.

Now let's consider this approach: we write a simple two-line code to visualize a simple scatter plot, it can immediately reveal whether there’s a linear relationship between two continuous variables. The scatter plots data points on a two-dimensional graph, making it easy to see correlations, clusters, or outliers that might easily go unnoticed. However, we certainly need to run further statistical tests to confirm the apparent relationship we have observed but this is a smart and quick way to start exploring the dataset.

Example scenario: let’s say your dataset includes variables like age and income. By plotting age on the x-axis and income on the y-axis, a scatter plot can quickly show whether income tends to increase, decrease, or stay the same as age increases. This visual representation saves you from spending hours or more to manually explore these patterns.

When it comes to categorical variables, a simple box plot can summarize the data distribution, central tendency, and variability.

Example scenario: you have data on customer satisfaction ratings across different regions. In this context, a simple box plot can display the average satisfaction level in each region along with the spread of the ratings. One can detect any significant differences or anomalies present among them.

Hence, we can see how these two simple plots—the scatter plot for continuous variables and the box plot for categorical variables — can provide valuable insights quickly. It helps us turn a complex data sheet into a story. Even a point in a scatterplot contributes to the narrative.

In this article, we will discuss different visualization techniques especially used for tabular datasets. In a future article, we can discuss excellent techniques in natural language processing.