Basic Libraries for Data Science These are the basic libraries that transform Python from a general purpose programming language into a powerful and robust tool for data analysis and visualization.

Netflix's data-science team has open-sourced its Metaflow Python library, a key part of the 'human-centered' machine-learning infrastructure it uses for building and deploying data-science workflows.

Anaconda today announced support for Snowflake Notebooks, an interactive, cell-based data science notebook similar to Jupyter. The move will let data scientists, data analysts, and data engineers ...

Libraries are collections of shared code. They're common in Python, where they're also called "modules," but they're also ...

Python is the most popular programming language, outranking C and C++. Enterprises are using Python for HPC with the help of Intel Performance Libraries.

Nvidia wants to extend the success of the GPU beyond graphics and deep learning to the full data science experience. Open source Python library Dask is the key to this.

Although Julia is purpose-built for data science, whereas Python has more or less evolved into the role, Python offers some compelling advantages to the data scientist.

But with Python libraries, data solutions can be built much faster and with more reliability. SciKit-Learn, for example, has built-in algorithms for classification, regression, clustering, and ...

What are some use cases for which it would be beneficial to use Haskell, rather than R or Python, in data science? This question was originally answered on Quora by Tikhon Jelvis.