Before we write a single line of code, let’s get a general overview of what data visualization is and why it is crucial. This course focuses on the code only. However, it is essential to understand some of the fundamental concepts to help you better approach your projects.

What is data visualization?

Data visualization is the ability to convey a story or an idea as efficiently as possible. As the old saying goes:

A picture is worth a thousand words.

Data visualizations are not just for making data look good. They let an end-user better understand a situation or story. As a result, data visualization will allow the user to make an informative decision or learn something new.

Data visualizations can also help us explore patterns. Looking at a spreadsheet or table of data makes it harder for users to visualize trends. Without proper visualization, we can end up missing small disruptions in our data.

So, data visualization is an integral part of data science. If we were to break down most visualizations, we would find that visualizations are made up of shapes, colors, and sizes. With just these three building blocks, we give the user insight into the data they are viewing.

What makes a good visualization?

Here are five tips for making a great visualization.

Tip #1: Understand the context

Before you begin writing your code, it is essential to understand the data visualization context. You need to ask yourself the following questions. Who is the audience? What do they need to know? What do they need to do with the data? It is important to answer these questions before you start writing anything.

Tip #2: Choose an appropriate visualization

This can be considered the next step after you have answered the questions in the previous tip. You can only choose a visual after you know who your audience is. Some visualizations can better communicate data than others.

Tip #3: Remove clutter or unnecessary information

When you receive data, there is a possibility that the data contains information that is not important for visual display. This is common in maps. Cities and towns tend to be close to each other, but you do not want to draw every single one of them. If you did, then your map can become cluttered. Focus on the major cities to help give the reader an overall understanding of where things are.

Along with the data, you may have types that are not supposed to be displayed visually. Later in the course, you will understand what types are. For now, understand there are some situations where specific data should not be displayed.

Sometimes you will have data that is more important than others, so you should prioritize it visually.
Leading me to tip #4.

Tip #4: Draw attention using shape, size, and color.

If you have something that the user should focus on, do not be afraid to make it larger or make that piece of data stand out. It is a very straightforward tip, but many visual data designers do not bother making their visuals more interesting.

Tip #5: Tell a story with your data

This is something you will learn through experience and by studying other data visualizations. Some of the greatest data visuals tell a story. Check out this article by Google. It talks about the importance of telling a story with data.

At the very beginning, there is a quote that summarizes why telling a story is important.

Most organizations recognize that being a successful, data-driven company requires skilled developers and analysts. Fewer grasp how to use data to tell a meaningful story that resonates both intellectually and emotionally with an audience. Marketers are responsible for this story; as such, they’re often the bridge between the data and those who need to learn something from it or make decisions based on its analysis. As marketers, we can tailor the story to the audience and effectively use data visualization to compliment our narrative. We know that data is powerful. But with a good story, it’s unforgettable.

Read the rest of this article and watch the video to better understand this tip. So, we have gone over what data visualization is; let’s talk about how D3 plays into all of this.

What is D3?

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