Скачать или смотреть EDA: The Key to Unlocking Insights in Your Data - Part 2 | Data science in Tamil

EDA (Exploratory Data Analysis) is a critical step in data analysis that involves examining and visualizing data to identify patterns, trends, and relationships that can provide insight into the data and guide further analysis. Here are the general steps involved in the EDA process:

Data Collection: The first step in EDA is to collect the relevant data from different sources, such as databases, APIs, or flat files.

Data Cleaning: After collecting the data, it needs to be cleaned by removing any missing values, duplicates, or outliers.

Data Visualization: Once the data has been cleaned, it is visualized using various techniques such as histograms, scatter plots, box plots, and heatmaps to identify patterns, trends, and outliers.

Data Analysis: After visualizing the data, statistical analysis is performed to understand the relationships between different variables, such as correlation, regression, or clustering.

Interpretation and Communication: Finally, the results of the analysis are interpreted and communicated to stakeholders using various tools and techniques, such as reports, dashboards, or interactive visualizations.

Overall, the EDA process provides a powerful way to explore and understand data, identify patterns and relationships, and communicate insights to stakeholders. It is an iterative process that involves continuous exploration and refinement of the data to gain deeper insights and understanding.