This content originally appeared on DEV Community and was authored by Muthoni, Rogers
Introduction
Exploratory Data Analysis (EDA) is the art of investigating a dataset to discover its main characteristics. It is an important step in every data analysis project. Data professionals often employ EDA techniques to see what the data can reveal while understanding the relationship between different variables in the dataset. Depending on size interpreting and comprehending datasets can be a challenging task. It would not be feasible to make inferences by looking at the first hundred records from a thousand or even a million of them. Through EDA techniques, it becomes easier to extract summaries and find critical and relevant data points that can guide further steps in data analysis projects. In this article, I will discuss various EDA techniques used by data professionals including different methods of summarizing and visualizing data, detecting outliers, and finding correlations.
EDA Techniques
There are several techniques that form the backbone of EDA. The essential techniques include; summary statistics, analysis of missing data, detection of outliers, correlation analysis, data visualization, time series analysis, exploration of categorical data, and dimensionality reduction. An in-depth discussion of these techniques has been made below.
- Summary Statistics
Summary statistics are useful when you want to get a quick overview of your data. They provide information such as the measures of location and spread. Measures of location (also measures of central tendency) provide information on where the data points are located. The particular measures of location include the mean, median, and mode of the data. Measures of spread tell how data points in the datasets are varied or spread out. The particular measures of spread include quartiles, range, interquartile range, variance, and standard deviation.
- Analysis of Missing Data
- Detection of Outliers
- Correlation Analysis
- Data Visualization
- Time Series Analysis
- Categorical Data Analysis
- Dimensionality Reduction
This content originally appeared on DEV Community and was authored by Muthoni, Rogers
Muthoni, Rogers | Sciencx (2024-08-11T12:48:00+00:00) Understanding Your Data: The Essentials of Exploratory Data Analysis (EDA). Retrieved from https://www.scien.cx/2024/08/11/understanding-your-data-the-essentials-of-exploratory-data-analysis-eda/
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