What is Data Visualization?

Data Vizualisation in Machine Learning and Data Science

Data visualization is very important topic in machine learning and data science. Data visualization, as the name suggests, is the graphical representation of data that helps in understanding data and creates more information for analysing.

In this article, I would be giving you a detailed explanation about Data visualization.

What is Data Visualization?

The process of representing data using popular visuals like charts, plots, and infographics is known as data visualization. These data visualizations helps in understanding complex datasets with ease.

Types of data

Data visualization is critical in market research because it allows both numerical and categorical data to be visualized, increasing the impact of insights and lowering the risk of analysis paralysis. As a result, data visualization is classified as follows:

Types of plots in data visualization

Tables

• Table has columns and rows, which are used to compare different variables. Tables have the ability to present data in an organised manner.

Pie charts and stacked bar charts

• Each segment of these graphs represents a separate element of a dataset. They make it straightforward to compare the sizes of various components and organise data.

Line graphs and area charts

• Line graphs employ lines to represent these changes, whereas area charts use line segments to link data points, stacking variables on top of one another and utilising colour to distinguish between variables.

Histograms

• This graph shows a distribution of data, reflecting the quantity of data that falls inside a specific range, using a bar chart (with no gaps between the bars).

Scatter plots

• These types of graphs are commonly used in regression data analysis because they are helpful for determining the connection between two variables.

Tree maps

• Hierarchical data is displayed using tree maps as a collection of nested forms, most frequently rectangles. When comparing proportions between categories depending on the size of respective territories, treemaps are helpful.

There are a variety of plots available, including heat maps, box plots, and others.

Advantages of Data visualization in machine learning and data science models

• A Better Method: It overcomes the challenge of incorporating information from both perspectives into the pictorial . This provides a better way to understand the dataset.
• Finding patterns: A large amount of data is processed and visualized in order to identify patterns. Many useful insights and data-backed evidence can be used to establish goals.
• Finding Connections Between Data: Finding a link between these large datasets allows programmers to better understand the problems they face.

Popular Python libraries for data visualization

Matplotlib

Matplotlib is the most well-known and widely-used charting library in the Python community, a data visualisation and 2-D plotting library used to plot mostly types of graphs and visuals.

Plotly

Plotly is another well known library used to plot graphs it has more tha 40 types of charts type.

Seaborn

Seaborn is a Python data visualisation library. It offers a sophisticated drawing tool for creating eye-catching and educational statistics visuals.

conclusion

In this article, I’ve covered what data visualization is, its benefits, and some plots. So, I hope you guys have a good understanding of data visualization today. In the near future, I’ll be writing more articles in which I’ll explain more models and how to implement data visualizations using Matplotlib with source code.