Design thinking is critical for developing data-driven business tools that surpass end-user expectations. Here's how to apply the five stages of design thinking in your data science projects.

The work was supported by the Air Force Office of Scientific Research, the Office of Naval Research, and the US National Science Foundation. Most approaches to machine learning-based inverse design ...

Meeting The Data Needs Of AI The data science and machine learning technology space is undergoing rapid changes, fueled primarily by the wave of generative AI and—just in the last year—agentic ...

The global market for data science platforms, meanwhile, was valued at $4.7 billion in 2020 and is projected to reach $79.7 billion by 2030, a CAGR of 33.6 percent, according to an Allied Market ...

If you rotate an image of a molecular structure, a human can tell the rotated image is still the same molecule, but a machine ...

A new computing architecture enables advanced machine-learning computations to be performed on a low-power, memory-constrained edge device. The technique may enable self-driving cars to make ...

Grammarly adopted the medallion architecture while migrating from their in-house data lake, storing Parquet files in AWS S3, to the Delta Lake lakehouse. The company created a new event store for over ...

One of the key obstacles to efficient quantum machine learning has been encoding classical data into quantum states, a computationally challenging task requiring deeply entangled circuits. To ...

Two new studies from the Department of Computational Biomedicine at Cedars-Sinai are advancing what we know about using ...

Domino Data Lab Named a Visionary for Second Consecutive Year in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms Provided by PR Newswire May 30, 2025, 6:41:00 AM ...