DSDE: In Theory

Data volumes are growing at an exponential rate. How can high-performance computing solutions help operators manage these volumes? Will faster, stronger processors and cloud computing solutions be the answer?

While the visual element is key, the core strategic component of data visualization is the ability to unlock the story in the data.

Instinctively, we feel that greater accuracy is better and all else should be subjected to this overriding goal. This is not so. While there are a few tasks for which a change in the second decimal place in accuracy might actually matter, for most tasks, this improvement will be irrelevant.

As we move to digitize our visual inspections with a variety of image-capture devices, fully understanding the strengths and limitations of the approach is important to move truly from a qualitative to a quantitative assessment with confidence.

Ridding science of shoddy statistics will require scrutiny of every step, not merely the last one.

When you think of “data science” and “machine learning,” do the two terms blur together? This article will clarify some important and often-overlooked distinctions between the two to help better focus learning and hiring.

Batch data processing is extremely challenging. It’s time-consuming, brittle, and often unrewarding. This story explores how applying the functional programming paradigm to data engineering can bring clarity to the process.