In this article, we’ll introduce you to some of the libraries that have helped make Python the most popular language for data science in Stack Overflow’s 2016 developer poll.

Python can be made faster by way of external libraries, third-party JIT compilers (PyPy), and optimizations with tools like Cython, but Julia is designed to be faster right out of the gate.

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

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.

The annual Python Developers Survey shows a programming environment in transition. Data science accounts for more than half ...

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.

Find out what makes Python a versatile powerhouse for modern software development—from data science to machine learning, systems automation, web and API development, and more.

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 ...