Ruibai Kuang: The Path to data science visualization

Today, the concept of big data is increasingly mentioned, and data visualization has also been put on the agenda.

Visualization has become the best way to understand data (or the only way), and if we don't visualize it, we will fall behind.

People use computers to create graphical charts, visually extract data, and present various attributes and variables of data. With the development of computer hardware, people have created more complex and larger digital models and developed data collection devices and data storage devices. Similarly, more advanced computer graphics technologies and methods are required to create these large datasets. As the data visualization platform expands, the application field increases, and the forms of expression change, as well as real-time dynamic effects and user interaction, data Visualization expands boundaries like all emerging concepts.

However, improper visualization is often counterproductive. Qualified Data Visualization has news value, that is, it must help the target audience better understand the data. Some data visualization only allows us to see cool and arrogant graphics or dense data. These are the ways to focus too much on the artistic and scientific nature, while ignoring the fundamental purpose. In the theory of information research, data seems too messy and intensive, and users will "Cut off data transmission 」.

The best way to avoid these errors is to focus on your goals first. Consider these issues in the following order before you consider what visual effects you should present.

What actions do you need to enable? What decision do you need to notify? What kind of questions do you need to ask? What data do you need to see? What is the optimal structure for revealing important relationships in data? What data do you need to highlight?

When you answer these questions, you can begin to use the correct data to design and implement the correct visual effects. You may have to make some changes. This is a good thing. Repeat, test, try different methods, test more methods, and repeat them again. A well-thought-out, user-oriented design method will produce effective, efficient, and useful visual data.