Most Important Data Visualizations in Data Science

Data visualization helps to understand the data you are working on. Data is a collection of numbers, and we humans can better understand the relationship between characteristics by visualizing them rather than simply comparing numbers. In this article, I’ll walk you through the most important data visualizations in data science and how to visualize them using Python.

Most Important Data Visualizations in Data Science

Below are the most important data visualizations that every data science professional must know:

  1. Bar Chart
  2. Histograms and Density Plots
  3. Heatmaps
  4. Scatter Plot
  5. Time Series Graph (Line Plot)
  6. Choropleth Maps

So these are the most important data visualizations in data science that you need to know about. Now, in the section below, I’ll walk you through how to visualize all of these data visualizations using Python.

Most Important Data Visualizations using Python

Bar Charts:

Bar charts are used to examine the quantities associated with a particular set of items. They are used to represent categorical data with rectangular bars where the height or length of each bar is directly proportional to the value it represents. Here is how we can visualize bar charts using Python:

Histograms and Density Plots:

Histograms and density graphs are used to visualize the distribution of the data. They can both be viewed separately, but combining them gives a better picture of the distribution of your dataset. Here’s how to visualize histograms and density plots using Python:

Heatmaps:

Heatmaps are one of the best data visualizations used to understand the correlation between characteristics of a dataset. Besides simple correlation, we can also analyze variance, anomalies, and various other patterns using heatmaps. Here’s how we can visualize a heatmap using Python:

Scatter Plots:

Scatter plots are one of the most common ways to analyze multidimensional data. In data science, a scatter plot is very commonly used to analyze the relationships between two entities. It can also be used to detect outliers in your data set. Here is how we can visualize a scatter plot using Python:

Time Series Graphs:

Time-series graphs are line charts that are used to show repeated measurements taken at regular time intervals. In Data Science, we use a time-series graph mainly when analyzing stock prices, income generated over time, sales, trends, etc. When viewing a time-series graph, the time is always taken on the x-axis and data points are always taken on the y-axis. Here’s how we can visualize a time series chart using Python:

Choropleth Maps:

Choropleth Maps are used to analyze the distribution of a feature in a geographical area. It is a shaded map where the intensity of the colours represents the intensity of the distribution of the features on that geographical area. Below is how we can visualize a choropleth map using Python:

Summary

Data visualization helps to understand the data you are working on. Data is a collection of numbers, and we humans can better understand the relationship between characteristics by visualizing them rather than simply comparing numbers. I hope you liked this article on the most important data visualizations in data science and how to visualize them using Python. Feel free to ask your valuable questions in the comments section below.

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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
Articles: 1102

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