EXPLORATORY DATA ANALYSIS USING DATA VISUALIZATION TECHNIQUES
Tags: datascience, beginners, learning, machinelearning
Published: 2023-10-16 16:02:25

One of the many gifts of technological advancement is Exploratory Data Analysis (EDA). It has provided us with the ability to make informed decisions and also give us visuals of how to make this decisions explainable to all. In this article, we will explore what EDA means, its importance, the common Data Visualization techniques, and some of the best practices in EDA.


**What is Exploratory Data Analysis?**
Exploratory Data Analysis (EDA) is a crucial initial step in the data analysis process. In order to find patterns, anomalies, correlations, and other insights, it requires the statistical and visual exploration of data. Data visualization techniques are essential to EDA because they assist turn unprocessed data into understandable visual representations. In order to comprehend a dataset better, we'll discuss the value of EDA in this post and show how to use various data visualization techniques.

**The Importance of EDA**
EDA serves several essential purposes in data analysis:
1). **Data Understanding**: It aids in your familiarization with your data's structure and salient characteristics. To find any problems or oddities in the dataset, it is essential to complete this phase.
2). **Data Cleaning**: EDA frequently discovers data anomalies, outliers, or discrepancies. For correct modeling, these problems must be found and fixed.
3). **Feature Selection**: By choosing the most crucial variables for analysis, EDA enables you to pinpoint pertinent characteristics and reduce dimensionality.
4). **Pattern Discovery**: EDA reveals correlations, patterns, and trends in the data. Decision-making insights can be gained from this.
5). **Model Assumptions**: EDA aids in confirming or refuting predictions made about the data during later phases of research, including statistical modeling.

**Data Visualization Techniques for EDA**

Some of the data visualization techniques commonly used in EDA are:

1). **Histograms**
Histograms provide a visual representation of the distribution of a single variable. They aid in your comprehension of the data's distribution, form, and central tendency. For instance, a skewed distribution may be a sign of outliers.

[Histogram](https://th.bing.com/th/id/R.268ccb4bd0b7786cb89bd7799ca66331?rik=0WM4xH4YpFgAfQ&riu=http%3a%2f%2fcdn.ttgtmedia.com%2fdigitalguide%2fimages%2fMisc%2fiw_histogram.gif&ehk=e71RaICBp3ep7nsMX2xY4FoVjHnH3PxsgducA4JgQgs%3d&risl=&pid=ImgRaw&r=0&sres=1&sresct=1)

2). **Box Plots**
Box plots display the distribution of a variable's values, showing the median, quartiles, and potential outliers. They are valuable for spotting variation and contrasting distributions between various categories or groupings.

[Box Plots](https://cdn-images-1.medium.com/max/800/1*rITjd02CxQqpQx8ANrjTag.png)

3). **Scatter Plots**
Scatter plots visualize the relationship between two numerical variables. They can highlight patterns like clusters, outliers, or correlations. Additional dimensions can be incorporated by using color coding or size markers.


[Scatter Plots](https://th.bing.com/th/id/R.2e79dc84304eb4207386a2d875c3964f?rik=6Th3OUf%2bok%2bEfQ&pid=ImgRaw&r=0)

4). **Line Plots**
Line plots are often used to display time series data. They aid in recognizing trends, seasonality, and long-term changes. In the analysis of financial, stock market, and climatic data, line charts are useful.

[Line Plots](https://th.bing.com/th/id/R.4752f2647ecb603e7a89c5bda5deba9a?rik=F6lyfmWI%2bwWANA&pid=ImgRaw&r=0)

5). **Bar Plots**
Bar plots represent categorical data with bars, making it easy to compare values across categories. They are helpful for representing averages, proportions, or numbers.
Other types of data visualization techniques used in EDA are Heatmaps, Pair plots, Violin plots, and so on.

[Bar Plots](https://th.bing.com/th/id/R.70450e5376450f785accea721e2a1369?rik=nIkRzZsxH71GeA&pid=ImgRaw&r=0)

**Best Practices in EDA**

Here are some of the following practices when you are working on EDA;

1). **Combine Visualization and Summary Statistics**: To fully comprehend the data, combine summary statistics with visualizations.
2). **Document Your Findings**: Record your findings, patterns, and any data problems you come across along the EDA process. This documentation can serve as a roadmap for additional investigation and judgment.
3). **Iterative Process**: EDA is often iterative. You might need to go back and edit your analysis when you get new insights.
4). **Use Domain Knowledge**: Apply subject expertise to your interpretation of the results and to the queries you pose to the data.
5). **Consider Data Transformation**: In some circumstances, altering variables (such as calculating logarithms) might help to highlight patterns.
6). **Storytelling**: Consider telling a data-driven tale that other people can easily understand when presenting your EDA results.

Exploratory Data Analysis is a vital stage of data analysis that enables data scientists and analysts to uncover undiscovered ideas, spot problems with the quality of the data, and make defensible choices on further modeling or analysis. By using data visualization approaches, you can better comprehend your data and realize its potential for producing insightful knowledge.