In the field of data science, data visualization is undoubtedly the top word today. No matter what data you want to analyze, doing data visualization seems to be a necessary step. But many people don’t have a specific concept of data visualization, and they don’t know how to implement it. So, today I am going to take you through the definition, concept, implementation process and tools for data visualization.
D3 has powerful SVG operation capability. It can easily map data to SVG attribute, and it integrates a large number of tools and methods for data processing, layout algorithms and calculating graphics. It has a strong community and rich demos. However, its API is too low-level. There isn’t much re-usability while the cost of learning and use is high.
It can be specifically targeted for map applications, and it has good compatibility with mobile. The API supports plug-in mechanism, but the function is relatively simple. Users need to have secondary development capabilities.
Vega is a set of interactive graphical grammars that define the mapping rules from data to graphic, common interaction grammars, and common graphical elements. Users can freely combine Vega grammars to build a variety of charts.
Based entirely on JSON grammar, Vega provides mapping rules from data to graphics, and it supports common interaction grammars. But the grammar design is complex, and the cost of use and learning is high.
deck.gl is a visual class library based on WebGL for big data analytics. It is developed by the visualization team of Uber.
deck.gl focuses on 3D map visualization. There are many built-in geographic information visualization common scenes. It supports visualization of large-scale data. But the users need to have knowledge of WebGL and the layer expansion is more complicated.
5. Power BI
Power BI is a set of business analysis tools that provide insights in the organization. It can connect hundreds of data sources, simplify data preparation and provide instant analysis. Organizations can view reports generated by Power BI on web and mobile devices.
Power BI is similar to Excel’s desktop BI tool, while the function is more powerful than Excel. It supports for multiple data sources. The price is not high. But it can only be used as a separate BI tool, and there is no way to integrate it with existing systems.
The use threshold is very low. HighCharts has good compatibility, and it is mature and widely used. However, the style is old, and it is difficult to expand charts. And the commercial use requires the purchase of copyright.
Tableau is a business intelligence tool for visually analyzing data. Users can create and distribute interactive and shareable dashboards, depicting trends, changes and densities of data in graphs and charts. Tableau can connect to files, relational data sources and big data sources to get and process data.
Tableau is the simplest business intelligence tool in the desktop system. It doesn’t force users to write custom code. The software allows data mixing and real-time collaboration. But it’s expensive and it performs less well in customization and after-sales services.
Data visualization is a huge field with many disciplines. It is precisely because of this interdisciplinary nature that the visualization field is full of vitality and opportunities.