Data Science Articles

Matthew Urwin

Updated on May 01, 2026

Google is reportedly expanding its partnership with the Pentagon, allowing its models to be used in classified settings while giving up control over how the technology is ultimately deployed in military and intelligence operations.

Dario Radečić

Updated on April 29, 2026

Pandas offers three methods to add a row to a DataFrame, including the append() method, loc[] property and the concat() method. Here’s how to do each one.

Mae Rice

Updated on April 29, 2026

Big data has become an essential tool in many industries, ranging from marketing to healthcare. Here are some real-life examples of big data impacting everyday life.

Matthew Urwin

Updated on April 28, 2026

Humanoid robots are about to level up with “AI brains” — AI systems powered by advanced models that can learn to adapt to new situations. They’re still a work in progress, but they could be a sign that the skilled trades aren’t so AI-proof after all.

Jeff Rumage

Updated on April 28, 2026

As AI data centers devour electricity at an unprecedented rate, tech giants are racing to secure energy by reviving shuttered nuclear plants, investing in small modular reactors and experimenting with green energy alternatives.

Jeff Rumage

Updated on April 27, 2026

The content you share online may be scraped to train AI models. Opting out can help you maintain control over your data, reduce the risk of unintended exposure and prevent your work from being reused without consent.

Jeff Rumage

Updated on April 22, 2026

Palantir’s data analysis software is known as a military intelligence tool. But as the company branches out in the government and corporate America, we take a closer look at its technology.

Brooke Becher

Updated on April 21, 2026

Billions of dollars are changing hands between the same few AI companies. That affects everything from compute costs to market volatility — and it’s worth understanding.

Edward Hearn

Updated on April 20, 2026

Ordinal data can be classified and ranked, whereas nominal data is categorized without any specific hierarchy. Let’s explore how these two data types stack up against each other and others.

Ankush Rastogi

Updated on April 16, 2026

Too many organizations understand the AI skills gap backwards, focusing too much on the output layer. Most AI projects that fail go astray because of bad data, and that’s where you need to focus.