Microsoft is extending Dataverse into coding-agent marketplaces while expanding its MCP tools, certification program and governance controls.

A practical roadmap for data science beginners, covering fundamentals, key libraries, projects, and advanced skills. It focuses on real-world learning, avoiding common mistakes, and building job-ready ...

PyCharm, DataSpell, and VS Code offer strong features for large projects. JupyterLab and Google Colab simplify data exploration and visualization. Thonny, Rodeo, and Sublime Text are good for ...

Already using NumPy, Pandas, and Scikit-learn? Here are seven more powerful data wrangling tools that deserve a place in your toolkit. Python’s rich ecosystem of data science tools is a big draw for ...

Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. This article shows data engineers how to use PyIceberg, a lightweight and powerful Python library ...

Python libraries are pre-written collections of code designed to simplify programming by providing ready-made functions for specific tasks. They eliminate the need to write repetitive code and cover ...

A few years ago a new pair of Profiler events was added for Power BI Import mode datasets (and indeed AAS models): the Job Graph events. I blogged about them here but they never got used by anyone ...

Mypy, Pytype, Pyright, and Pyre can help you keep your type-hinted Python code bug-free. Let’s see what each of these useful tools has to offer. In the beginning, Python had no type decorations. That ...

Dr. James McCaffrey of Microsoft Research says the main advantage of scikit is that it's easy to use (even though most classes have many constructor parameters). Logistic regression is a machine ...