Exploratory Data Analysis (EDA)
Exploratory data analysis (EDA) is an approach to analyzing data that involves exploring and summarizing the main features of a dataset to gain a better understanding of its underlying structure and patterns. EDA is often the first step in the data analysis process and can help identify potential problems or anomalies in the data.
The main goal of EDA is to uncover interesting features and relationships in the data that may be useful for further analysis or modeling. This involves examining the data from different angles, using a range of statistical and graphical methods, and asking questions about the data to generate hypotheses.
Some common techniques used in EDA include:
Summary statistics: computing measures such as mean, median, mode, and standard deviation to describe the central tendency and variability of the data.
Visualization: creating charts, graphs, and plots to visualize the distribution and relationships among variables in the data.
Outlier detection: identifying data points that are significantly different from the rest of the data, which may indicate errors or important features of the data.
Dimensionality reduction: reducing the number of variables in the data to better understand its structure and patterns.
EDA is an essential tool in data analysis, as it helps researchers and analysts gain insights into the data and generate hypotheses that can be tested and further explored.
The main goal of EDA is to uncover interesting features and relationships in the data that may be useful for further analysis or modeling. This involves examining the data from different angles, using a range of statistical and graphical methods, and asking questions about the data to generate hypotheses.
Some common techniques used in EDA include:
Summary statistics: computing measures such as mean, median, mode, and standard deviation to describe the central tendency and variability of the data.
Visualization: creating charts, graphs, and plots to visualize the distribution and relationships among variables in the data.
Outlier detection: identifying data points that are significantly different from the rest of the data, which may indicate errors or important features of the data.
Dimensionality reduction: reducing the number of variables in the data to better understand its structure and patterns.
EDA is an essential tool in data analysis, as it helps researchers and analysts gain insights into the data and generate hypotheses that can be tested and further explored.
