Data science techniques are the tools in a data scientist's toolbox — each one suited to a specific kind of problem. Whether it's sorting, predicting, or discovering hidden patterns, there's a method ...

It’s time for universities to stop raising tuition and start cutting costs. For years, the narrative of “state disinvestment ...

Overview: A data scientist must blend technical skills with business sense.Tools and techniques evolve, but core ...

Multicollinearity in Regression Analysis: The Problem Revisited Donald E. Farrar and Robert R. Glauber The Review of Economics and Statistics , pp. 92-107 (16 pages) ...

Based on data from its call center, a warranty company thought its market was predominantly female. However, when that ...

Supervised learning is a machine learning approach in which algorithms are trained on labelled datasets—that is, data that already includes the correct outputs or classifications. The model learns to ...

Artificial intelligence has been built on the idea that giving machines more time, data, and computing power improves their performance. This belief has guided the direction of AI research and ...

A new reasoning model quantifies how often large language models elaborate on false clinical details fed to them. Prompt ...

This is a valuable computational study of odor responses in the early olfactory system of insects and vertebrates. The study addresses the question of how information about odor concentration is ...

Generative AI - Intern (Personalisation & CI) ING's goal is to enable people to "make the difference" and empower them to stay a step ahead in life and in business. We are one of the largest banks in ...

Wild fire is a serious environmental and socioeconomic menace and therefore any effort aimed at the early and accurate detection of wild fire has to be very useful. This paper proposes a novel machine ...