The problem with notebooks is that they're much better for experimental data science work than they are for production data engineering work. That's my own opinion, of course. But I stand by it.

Originally developed for data science applications written in Python, R, and Julia, Jupyter Notebook is useful in all kinds of ways for all kinds of projects: Data visualizations.

Jupyter Notebooks are where data science starts — and now that they are included as part of Civis Platform, that exploration is only the beginning. As a product manager for Civis Platform, I pay ...

“Customers want a notebook solution that will allow them to focus on their data science work rather than infrastructure management,” said Venky Veeraraghavan, SVP of product at DataRobot. “With ...

Open Excel, and you will see a new tab in the ribbon at the top that says PyXLL.

Others in the academic environment can also benefit from Jupyter Notebook’s user-friendly interface, which democratizes data science. Professors and students in just about every discipline, from ...

Jupyter Notebook is an open source web environment for data visualization. The modular software is used to model data in data science, computing, and machine learning.

Deepnote, a startup that is building a data science platform on top of Jupyter-compatible notebooks, today announced that it has raised a $20 million Series A round co-led by Index Ventures and ...

Datalore combines the power of the Jupyter data science notebook with PyCharm, JetBrain’s integrated development environment (IDE) for Python. This gives users smart coding features from the PyCharm ...

Jupyter Notebooks, even though tightly tied to data science darling programming language Python, can now be done with .NET languages C# or F#. The popular notebooks provide interactive environments -- ...