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.

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.

These libraries address various topics, including scientific computing, web development, graphical user interfaces (GUI), data manipulation and machine learning.

A key part of CUDA-X AI is RAPIDS. RAPIDS is a suite of open-source software libraries for executing end-to-end data science and analytics pipelines entirely on GPUs. And a key part of RAPIDS is Dask.

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, ...

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) ...

While core pieces—like Spark bindings—exist, they are rarely as well-supported as in more popular data science languages. The same is true of statistical libraries and data visualization.