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.

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

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.

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.

Because when you combine Python with the Numba just-in-time (JIT) compiler, the Cython compiler, and runtime packages built on Intel performance libraries such as Intel Math Kernel Library (Intel MKL) ...

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

In contrast, Python follows a multiprogramming paradigm, which makes it easy for developers to write concise code using syntactic sugar. Python was not built specifically for data science workloads, ...

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.

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 ...