What Is Data Visualization?
Data visualization is the final piece and skill set for accomplished data scientists and data analysts. It involves communicating their findings effectively through graphical means. So that the layman, often a business analyst or corporate executive, can comprehend the data scientist’s complex findings, a comprehensive presentation is developed. It might include graphs, charts, mind maps, infographics, and other visuals to help convey key data findings and insights.
Data visualization pulls from various disciplines, including information graphics, scientific visualization, and statistical graphics (graphical techniques). Data visualization is fast becoming a field unto itself, replete with training courses, manuals, guidebooks, and even a “Periodic Table of Visualization Methods,” an interactive chart displaying the various data visualization methods. These methods include data, information, concept, strategy, metaphor, and compound.
Different Approaches to Data Visualization
There are a number of different approaches to data visualization. The most common is the information presentation, which typically includes statistical graphics and thematic cartography. This approach encompasses seven areas of concentration:
- Displaying news
- Displaying data
- Displaying connections
- Displaying websites
- Mind maps
- Articles and resources
- Tools and services
A more scientific approach to data visualization focuses more on computer science and may emphasize the following:
- Visualization algorithms and techniques
- Volume visualization (2D and 3D renderings)
- Information visualization (visuals of abstract data)
- Modeling techniques (for business efficiency)
- Multi-resolution methods (data modeling algorithms)
- Interaction techniques and architectures
Data Visualization Applications
Data visualization, while a practice all its own, is typically incorporated as the final step with the work of other related fields, including: