Visualization in the Era of Big Data
The beginning and end of nearly any problem in data science is visualization— first, for understanding the shape and structure of the raw data and, second, for communicating the final results to drive decision making. In either case, the goal is to expose the essential properties of the data in a way that can be perceived and understood by the human visual system.
Traditional visualization systems and techniques were designed in an era of data scarcity, but in today’s Big Data world of an incredible abundance of information, understanding is the key commodity. Older approaches focused on rendering individual data points faithfully, which was appropriate for the small data sets previously available.
However, when inappropriately applied to large data sets, these techniques suffer from systematic problems like overplotting, oversaturation, undersaturation, undersampling and underutilized dynamic range, all of which obscure the true properties of large data sets and lead to incorrect data-driven decisions. Fortunately, Anaconda is here to help with datashading technology that is designed to solve these problems head-on.
In this paper, you’ll learn why Open Data Science is the foundation to modernizing data analytics, and:
- How datashader helps tame the complexity of visualizing large amounts of data
- The approach and advantages of using datashading technology
- Examples of using datashader to visualize billions of points of data