The article titled "5 AI Skills That Will Keep Data Scientists Relevant in 2027" outlines five specific AI competencies, explaining what each skill addresses and providing runnable code snippets that readers can directly paste into a notebook. It serves as a practical guide for data scientists aiming to stay current with emerging AI technologies.
By Sara Nobrega
AI has accelerated data scientists’ productivity, but its influence extends beyond speed. The technology is reshaping who owns data, how judgment is exercised, and the overall career trajectory of data scientists. These changes signal a broader transformation in the field’s structure and responsibilities.
By Yu Dong
If you are a programmer and you don't feel "special" anymore, you are not alone The post The Era of No-Code AI: What You Need to Know appeared first on Towards Data Science .
By Mauro Di Pietro
The article outlines four practical applications of AI for PhD students: locating relevant citations, consolidating code snippets, fact‑checking research claims, and preparing for the thesis defence. It highlights how AI tools can streamline the research process and improve the quality of academic work.
By Conor O'Sullivan
How AI has massively changed my day-to-day workflow The post A Day in the Life of a Data Scientist in 2026 appeared first on Towards Data Science .
By Haden Pelletier
As AI gets smarter, the real differentiator may be how well humans regulate their own thinking. The post Meta-Cognitive Regulation Might Be the Most Important AI Skill Nobody Is Talking About appeared first on Towards Data Science .
By Rashi Desai