Data visualization plays two key roles:
Communicating results clearly to a general audience.
2. Organizing a view of data that suggests a new hypothesis or the next step in a project.
This blog instructs of 10 visualizations you can bring to bear on your data:
1. Histograms
2. Bar/Pie charts
3. Scatter plots
4. Time series
5. Relationship maps
6. Heat maps
7. Geo Maps
8. 3-D Plots
9. Word Cloud
10. Higher-Dimensional plot
Histogram:
Head start with the overview of Histogram visualization in Data Science. Whenever there is a prerequisite of data, the word statics will pop-up eventually, the term ‘Histogram’ will be flashed.
Histograms are all-power visualization tools which inspect for the numerical data who helps in underlying and understanding the data set. They help in the distribution of vast data and frequency of data values.
Bar and pie charts:
The bar chart represents the data set with the rectangular plot and the circular plot is represented by the Pie charts. Both of them promote the distribution of data.
Pie chart faces difficulty when there is a call for comparing multiple charts, while Bar charts are useful in such cases.
Scatter plots:
Scatter plots are the most convenient and simpler charts as they can be presented in a non-array format. They represent data in 2-dimensional(x,y)format. They display all individual data points.
These plots can be used to show the correlations of data. One can visualize features of one scatter plot with features of another, and this technique has immensely increased its power.
Time Series
These are the plots with time range where individual dots form a part of a line exclusively. Time series visualizes pump-dump of a data set, they also help in rendering sensor data.
Relationship Maps
These charts are those who help to delve the insights of the company’s data engagingly. Colour spectrum illustrates the relationship of the data set column.
The relationship between the dataset values is embodied with the thickness of chart lines. The thickness also concludes the frequency count of two columns.
Heat Map
These maps inhibit another snappy way to correlate 2-Dimensional representation of plots within a matrix and display the frequency of dataset with colours.
Heat maps are user magnificent because the points are grid cells and each cell has a colour frequency of the data distribution which pulls a special trend.
Geo Map
In modern times, maps have become an alluring way to visualize the data. Surprisingly, we are fond of maps and gets excited by visualizing the data in Google maps, Snapchat, etc.
Maps are a well-heeled way to visualize your data. By visualizing data in Geo map, we can easily interpret the location, else we would be hurting for hours to figure out exact data.
3-D plots
This visualization excites us more than anything! 3-D data plots are most common visualize and they are expanding concerning a scatter plot. They show a correlation between three variables and are marked closely to compose visualization.
These charts illustrate user-interactivity since it benefits with a view of rotation and resizes. 3-D plots help in a better understanding of the data with convenient results.
Word Clouds
A colossal amount of data is generated and is available as effortlessly as a free text. Word clouds are a graphical representation of word frequency that is used to highlight trending terms.
It gives greater prominence to words in a source text. This visualization can be helpful for a dataset like interviews, documents or other texts, and can boost evaluators with engaging insights.
Higher-dimensional plots
We have discussed a full retrospect of visualization such as how they benefit us with the graphical representation with one or two feature. With the usage of High-Dimensional plots, we can visualize four, five and more features at one.
For achieving this chart, we need to reduce its dimensionality using either principal component analysis (PCA) or t-Stochastic Neighbor embedding (t-SNE). PCA techniques help to find cluster boundaries in the data set.
