A University of Hawaiʻi at Mānoa student-led team has developed a new algorithm to help scientists determine direction in ...

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...

Extracting and analyzing relevant medical information from large-scale databases such as biobanks poses considerable challenges. To exploit such "big data," attempts have focused on large sampling ...

This program had absolutely nothing to do with race…but multi-variable equations.” That’s what Brett Goldstein, a former policeman for the Chicago Police Department (CPD) and current Urban Science ...

AI’s biggest constraint isn’t algorithms anymore. It’s data…specifically, high-quality, forward-looking data. It is the “Rare ...

As computing power has increased and data science has expanded into nearly every area of our lives, we have entered the age of the algorithm. While our personal and professional data is being compiled ...

Forbes contributors publish independent expert analyses and insights. I write about the broad intersection of data and society. The data-driven revolution is prefaced upon the idea that data and ...

Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, ...

There’s no doubt that data and algorithms play an important part in the modern workplace, but we shouldn’t forget the human component of our decisions. We have an overwhelming amount of number and ...

How to recognize and use array and list data structures in your Java programs. Which algorithms work best with different types of array and list data structures. Why some algorithms will work better ...