Title:
Utilizing Data Science for Exploratory Data Analysis in Economic Research: Uncovering Key Relationships and Insights

Authors:
Priyam Ganguly, USA

Abstract:

This study utilizes Tableau for Exploratory Data Analysis (EDA) to examine an economic dataset. EDA employs visual representations and summary statistics to summarize data characteristics, identify patterns, and test hypotheses. We demonstrate the application of EDA techniques to understand the relationships between Gross Domestic Product (GDP), inflation, lending interest rates, and BSE growth, enabling informed decision-making. Through data visualization, we uncover correlations and interdependencies within the dataset, providing valuable insights for economic analysis. This methodology facilitates well-informed decision-making by enabling the identification and examination of interdependencies and correlations within the dataset. Trends, abnormalities, and possible causal linkages that might not be readily apparent using conventional data analysis approaches might be found by data visualization. Policymakers, economists, and academics can use our analysis to help them make data-driven decisions by providing insightful information for economic analysis. We demonstrate the value of EDA in economic research and Tableau's usefulness as a tool for finding significant patterns in large, complicated datasets by presenting these links.

DOI: http://dx.doi.org/10.51505/ijaemr.2024.9504