By now, even the most casual generative-AI tinkerer knows that the specificity of one’s prompt determines, to a large extent, the quality of a model’s output—whether that’s a realistic image, an on-topic paragraph, or a code block that actually works. Many industry leaders claim that prompt engineering—the practice of tailoring…

The two pillars of data science—statistics-backed analysis and code—come with a whole range of constraints. Structure your query the wrong way, and you might mess up an entire pipeline. Apply a formula incorrectly, and the results of your test might no longer reflect reality. Working within constraints doesn’t have to…

When it comes to mastering complex topics in data science or machine learning, two things are generally true: there are no real shortcuts, and people’s learning rhythm can vary dramatically. …

How data helps us make sense of cities — It’s sometimes hard not to think of cities as clusters of problems we need to solve: housing, food security, and environmental impact come to mind. …

While ChatGPT and similar tools have monopolized much of our collective attention in recent months, large language models (LLMs)—the infrastructure behind all those chat outputs we endlessly share, screenshot, and occasionally shake our heads at—have remained in (relative) obscurity. On a certain level, it makes sense: when you watch a…

Not that long ago, any workflow that involved processing audio files—even a fairly simple task like transcribing a podcast episode—came with a set of tough choices. You could go manual (and waste hours, if not days, in the process), rely on a few clunky and ultimately underwhelming apps, or patch…

When new tools, research, and buzzwords arrive on the scene as quickly as they have in recent years, terms like “state of the art” and “best practices” can seem meaningless. Last month’s cutting-edge model might already feel outdated; workflows that made perfect sense for your last project can become obsolete…

TDS readers come from a staggering variety of professional and educational backgrounds, and join our community at different points of their data science journey. We’re especially proud to support data professionals at the earliest stages of their career, but we also know that there’s no universal definition for what “beginner”…

On the stakes and challenges of responsible innovation — It’s never been easy to distinguish real progress from hype when it comes to AI research, but we seem to have reached an inflection point in recent weeks. …