What Is Data Visualization?
Our eyes are always drawn to colors, and it becomes easy to identify patterns and colors. It allows you to single out the relationship in colors. Due to this (EDA), Exploratory data analysis is an essential part of data science and machine learning pipeline. For one to create a robust and valuable product using data, you need to explore the data, understand the relations among the variables, and the underlying structure of the data. This is where data visualization comes in to play.
In our article, we take a look at data visualization an important part of data science.
What is visualization?
Visualization is the formation of mental visual images. It is the act or process of interpreting in visual terms or of putting it into a visible form. It involves producing images that communicate the relationships among represented data to the viewers of the images. Communication is achieved, through the use of a systematic mapping between graphic marks and the data values. The need to visualize data is an important step. It enables one to gain insights and to draw conclusions on the visualized data. The decision-makers can see analytics that presented visually, so they can fully grasp the difficult concepts or identify new patterns. Big Data, data visualization tools, and technologies are essential to analyze massive amounts of information and make data-driven decisions.
In a question and answer, Simon Samuel is asked. will data visualization change the way we interact with data?
He responds by “Absolutely. Analysts will be looking for deeper insights into the data. And the tools will enhance an executive’s ability to access data and its insights more directly.”
How is it done?
When it comes to data visualization every platform offers a different approach. We look at python language that has a set of libraries that will assist us to visualize the data.
1.Matplotlib
Matplotlib is specifically good for creating basic graphs like line charts, bar charts, histograms, etc. if you have worked with data before you are aware of the line.
import matplotlib.pyplot as plt
This is how to import the library. This library is comprehensive for creating static, animated, and interactive visualizations in Python. regardless of the kind of data or task at hand this library plays a big role in visualization of data.
2. Seaborn
This is a Python data visualization library that is based on matplotlib library. It provides a high-level interface for drawing attractive and informative statistical graphics. This is specifically good for creating heatmaps and boxplots, it is usually imparted as
import seaborn as sns
Seaborn aims at making visualization the central part of exploring and understanding data. Its a dataset-oriented plotting function that operate on data frames and arrays containing whole datasets.
3.Geoplot
This is a high-level Python geospatial plotting library. It is an extension of cartopy and matplotlib. Geoplot is a Python visualization library for plotting geographical data and the creation of maps.
It also comes with the following features:
High-level plotting API
Native projection support
Compatibility with matplotlib
4.Ploty
This is an open-source graphing library that is used to form data visualizations. Plotly is built on top of the JavaScript library (plotly.js). It enables Python users to create interactive web-based visualizations which are also displayable on the Jupyter notebooks. this version is the (ploty.py)
How to import the plotly library
import plotly.graph_objects as go
Plotly is used to create web-based data visualizations. Plotly provides numerous chart types like line charts, bar charts, scatter plots, histograms, pie charts, box plots, multiple axes, 3-D charts, etc. It also provides contour plots, which is a bonus compared to other data visualization libraries.
Summary
“Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights — look at data differently, more imaginatively. Data visualization will promote that creative data exploration.”
— Simon Samuel: The Head of Customer Value Modeling for a large bank in the UK
This indicates that without data visualization the data can become meaningless we require data visualization to draw out patterns and conclusion to the data.
Hope you liked our article leave a comment a like if you liked our article.
#happylearning #keeplearning
