What is data science visualization?
The visualization of data sciences refers to the techniques used to communicate data or information by coding it as visual objects contained in the graphs. The aim is to communicate information clearly and effectively to users to data science visualization
- The main goal of data visualization is to communicate information clearly and effectively through graphical means.
- It doesn’t mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful.
- Data visualization is closely related to information visualization, information graphics and scientific visualization.
- Data visualization has become an active area of research, teaching and development.
Data Science is more than just building predictive models – it is also about explaining the models and using them to help people to understand data and make decisions.
- Data visualization is an integral part of presenting data in a convincing way.
- There is a many of research on the proper visualization of data and the perception by people of information.
- From the point of view of Data Science, what makes the posting important is the highlighting of the key aspects of the data: what are the most important variables, their relative importance, what are the changes and trends.
- The visualization of the data should be visually appealing, but not at the expense of loading a graph with unnecessary garbage, as shown in the image on the right Good data science visualization
Why is data visualization important?
Due to the way the human brain processes information, the use of graphics or graphics to view large amounts of complex data is easier than browsing spreadsheets or reports. Data visualization is a quick and simple way to convey concepts in a universal way – and you can experiment with different scenarios by making small adjustments. Data visualization can also:
- Identify areas requiring attention or improvement.
- Identify factors that influence client behavior.
- Help you understand the products to be placed. Predict sales volumes
One common error is to change the axis to increase the size of effect.
Identical data , different axis. Okay, we mention how to avoid making a poor visualization.
How do we get good visualization of the data?
To do this, choose the right type of chart for your data:
- Line graphs to track changes or trends over time and show the relationship between two or more variables.
- Bar Charts to compare the quantities of different categories.
- Scatter Plots shows the joint variation of two data elements.
- Pie Charts to compare parts of a set – used them sparingly, as people struggle to compare the area of pie slice.
- You can display additional variables on a two-dimensional graph using color, shape, and size.
- Use interactive dashboards to allow experiments with key variables. This makes the good datascience visualization