5 Must-Know Data Visualization Techniques for Any Data Science Professional

1. Quick Data Visualization

In situations where data visualization is needed promptly, creating visuals directly through Python is the best route. It can be done quicker than setting up colorful, artistic visualizations through specialized software, using a few extra lines of code. This is often the first method among data visualization techniques that data scientists use to create visuals. While it may only create basic visualizations, this is often all that’s needed.

2. Presenting Data for Non-Technical Colleagues

One of the key benefits of data visualization is that it makes otherwise complex data easy to understand. In fact, it has even been proven to influence decision-making in business environments.


3. Showing Unique Data Sets

Some kinds of data may benefit from unique data visualization techniques and approaches in place of a more standard chart or graph. It can be tempting to go with basic scatter plots or pie charts no matter the data set being represented.

4. Presenting Complex Data Sets

The main idea of data visualization is to make complicated data easier to comprehend and therefore easier to apply and utilize. This can be more challenging for data that is inherently complex, though, even when communicating with other data scientists. Representing complex data requires its own unique approach, especially if the data visuals are for non-technical colleagues.

5. Creating Accessible Data Visualization

When creating data visualizations, it is important to consider everyone who may need to understand the visuals, including those with visual limitations. Examples include people with forms of color blindness or partial blindness. These people are important team members as well and should be able to benefit from visualizations just as much as their colleagues. Data scientists who are able to create accessible visualizations will come across as more perceptive and professional, as well.

Creating Better Data Visualizations

Data visualization is an integral part of data science. It allows the impact of data sets to be understood and experienced by everyone involved with a project. Visuals can be highly influential and compelling, but remembering to communicate in a way that is clear and accessible is crucial, no matter the data type. Using these data visualization techniques and tips will get you started on the path to creating data visualizations that are polished, effective, and engaging for all.



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