Data Visualisation — making data visible

Visualizing the data

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.

It is another form of visual art that grabs our interest and keeps our eyes on the message. When we see a chart, we quickly see trends and outliers. If we can see something, we internalize it quickly. It’s storytelling with a purpose.

Importance of Data Visualization

• Every STEM(Science, Technology, Engineering, Mathematics) field benefits from understanding data — and so do fields in government, finance, marketing, history, consumer goods, service industries, education, sports, and so on.
• Since visualization is so prolific, it’s also one of the most useful professional skills to develop. The better you can convey your points visually, whether in a dashboard or a slide deck, the better you can leverage that information.
• Skill sets are changing to accommodate a data-driven world. It is increasingly valuable for professionals to be able to use data to make decisions and use visuals to tell stories of when data informs the who, what, when, where, and how. While traditional education typically draws a distinct line between creative storytelling and technical analysis, the modern professional world also values those who can cross between the two: data visualization sits right in the middle of analysis and visual storytelling.

Different types of visualizations

• Charts
• Tables
• Graphs
• Maps
• Info-graphics
• Dashboards

Different types of Data Visualizations

Implementation

Few of the most popularly used plotting libraries are :

• Matplotlib: low level, provides lots of freedom
• Pandas Visualization: easy to use interface, built on Matplotlib
• Seaborn: high-level interface, great default styles
• ggplot: based on R’s ggplot2, uses Grammar of Graphics
• Plotly: can create interactive plots

Matplotlib

link : https://github.com/bhartendudubey/Data-Visualisation/blob/master/visual_matplotlib.ipynb

dataset : iris

Pandas Visualization

link : https://github.com/bhartendudubey/Data-Visualisation/blob/master/visual_pandas_visualisation.ipynb

dataset : iris

Seaborn

link: https://github.com/bhartendudubey/Data-Visualisation/blob/master/visual_seaborn.ipynb

dataset : iris

I will suggest that you should try these implementations for a better practical learning experience as well as better understanding of how Data Visualisation is helpful.