RAPIDS cuDF to Speed up Your Next Data Science Workflow - KDnuggets

Over the years there has been exponential growth in data science applications, fueled by data collected from a wide variety of sources. In the last 10 years alone we have seen the implementation of data science, machine learning and deep learning. Although we hear a lot more about machine learning and deep learning, it is the core data science technique that a lot of companies focus on as this is where they make money and save a lot of money. However, studies show that 68% of data studies go unused and 90% of data is left unstructured. This is due to companies failing to focus on the data analytical processing phase, as it can take a lot of time, money and resources.