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 ...

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 ...

Much of our reams of data sit in large databases of unstructured text. Finding insights among emails, text documents, and websites is extremely difficult unless we can search, characterize, and ...

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

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you''ve ever built a predictive model, worked on a ...

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 ...

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 ...

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, ...

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 ...

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 ...