Visualizations are a significant aspect of any data science project.

In Data Science, having a clear approach and orientation to solve the particular problem statement is crucial. Luckily, the tools required for Data Science offer the users and developers an excellent way to create to visualize the data and datasets to build effective models.

Thanks to visualization, most complex problems can be broken down into simpler elements for data scientists to figure out the optimal model architectures and solutions to complicated tasks. Hence, visualizations play a vital role in the successful completion of every major Data Science project. Without the use of visualization, it is nearly impossible to gauge the data patterns of a difficult task.

In this article, we will understand some of the basic features of data visualizations and try to interpret the benefits of exploratory data analysis while solving any kind of task. We will discuss a few essential libraries for visualization purposes. Then, we will have a detailed discussion on the eight best types of visualization techniques every Data Scientist should know about. Finally, we will conclude with a real-time example of interpreting these visualizations.

Understanding EDA:

In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It is an essential aspect of Data Science that you should consider while working on any kind of task. When you visualize the data, you gain an intuitive understanding, and your brain can perceive the various notions of their working standards. You also get several ideas and feedback on how you can work on these datasets.

Exploratory Data Analysis in Data Science is a great approach for developers to perform a detailed analysis on the available or collected datasets with the help of visualization techniques to obtain effective and efficient solutions. The exploratory data analysis step is considered to be the most important step to gain a procedural understanding of the further implementation of the data that is to occur in the future progress of the project.

While developing your projects or machine learning models, it is highly recommended that you take the exploratory data analysis step very seriously as it will help to reduce the workload and effort required for the particular task. If you know the essential elements to consider for the development of the project, it becomes easier for you to interpret, analyze, and develop.

Libraries:

Matplotlib.pyplot and seaborn are the two best library modules for visualization and performing exploratory data analysis tasks. These allow you to plot many graphical structures that are going to be extremely helpful for analyzing your data. These two libraries are some of the best approaches to solve almost any kind of visualization task of the available data in a Data Science project. They provide numerous applications and modules to solve multiple tasks.

Plotly is another great visualization tool that can be utilized by all data scientists for gaining high-quality visualizations. Plotly allows the users to get a glance at the 3-D visualizations to have a 3-Dimensional view of the overall dataset. This method allows the user to interpret the problem in a much more concise manner and ultimately develop amazing models.

The Tensorboard tool available in the Tensorflow deep learning framework is a fantastic way to the visualizations of the overall performance of deep learning model architectures that you have built. Using these graphs produced by Tensorboard, the developers can easily understand the interpretations of the train and validation data. They can figure out the over-fitting or under-fitting mechanisms to find alternative solutions.

8 Best Visualizations You Must Know As A Data Scientist:

Let us explore eight visualization techniques that every data scientist or Data Science enthusiast should totally know about. The eight visualization techniques described in this section will include an image as well as a small sample code block for a better understanding of these visualization methodologies. Let us begin exploring each of these concepts.

1. Bar Graph:

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