AI systems are getting easier to build, but harder to understand. As outputs become less predictable and workflows more ...

Scientists at EPFL have reimagined 3D printing by turning simple hydrogels into tough metals and ceramics. Their process allows multiple infusions of metal salts that form dense, high-strength ...

In 2026, Azure Machine Learning has evolved from a sandbox for data scientists into a robust platform for operational forecasting, yet many teams still struggle to see what happens after deployment.

Overview: Beginner projects focus on real datasets to build core skills such as data cleaning, exploration, and basic ...

FAANG data science interviews now focus heavily on SQL, business problem solving, product thinking, and system design instead ...

Heart Disease Analysis Power BI Dashboard A data-driven Power BI report analyzing heart disease patient data to uncover insights by gender, age, and health metrics. Built using Power BI, Excel, and ...

Python and R each excel in different aspects of data science—Python leads in machine learning, automation, and handling large datasets, while R is strong in statistical modeling and high-quality ...

Scientists have engineered a highly selective catalyst that can convert methane, a major component of natural gas, into methanol, an easily transportable liquid fuel, in a single, one-step reaction.

Experimental - This project is still in development, and not ready for the prime time. A minimal, secure Python interpreter written in Rust for use by AI. Monty avoids the cost, latency, complexity ...

Increasing your daily step count is more important for weight loss than the exact number of steps. Your body weight, effort, and pace affect how many calories you burn from walking. Adding intensity ...

aSydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia bThe Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia ...

Trusted quality data is the backbone of agentic AI. Identifying high-impact workflows to assign to AI agents is key to scaling adoption. Scaling agentic AI starts with rethinking how work gets done.