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 ...

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

Artificial intelligence can be a beautiful thing for business, with a lot of promise. But this promise has yet to deliver tangible results. Many AI projects fail in various stages of experimentation ...

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 ...

Through data, algorithms communicate with their environments and get to “know about” and “learn from” what is happening around them. Algorithms without living data are no more than sheer mathematical ...