Data visualization is the graphical representation of data using visual elements such as charts, graphs, maps, and dashboards to communicate information clearly and effectively. It transforms raw and complex datasets into meaningful visual patterns, enabling users to identify trends, relationships, distributions, and anomalies. In the field of Data Science and Big Data, data visualization is an essential step in exploratory data analysis, decision-making, and communication of insights. It allows analysts, managers, and stakeholders to interpret large volumes of structured and unstructured data quickly without examining individual records. The primary objective of data visualization is to simplify complex information and improve understanding. Human beings process visual information much faster than textual or numerical information. Effective visualizations reduce cognitive effort and support better business intelligence, scientific research, financial analysis, healthcare monitoring, and social media analytics. Modern visualization tools such as Tableau, Microsoft Power BI, Google Data Studio, Python libraries like Matplotlib and Seaborn, and R packages enable users to create interactive and dynamic visual representations of data. A bar chart is one of the most widely used visualization techniques. It represents categorical data using rectangular bars whose lengths are proportional to the values they represent. Bars may be displayed vertically or horizontally. Bar charts are suitable for comparing quantities across different categories, such as sales of products, student performance in different subjects, population across states, or