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.

Discover five powerful Python libraries that enable data scientists to interpret and explain machine learning models effectively.

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.

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.

This article rounds up some of the most valuable free data science courses offered by top institutions like Harvard, IBM, and ...

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 ...

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, ...

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

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.