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.

Programming languages Python and R are often pitted against each other over which is best for data science and analysis. Both are popular, although Python appears to be much more widely used, at ...

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.

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

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.

But Continuum Analytics in Austin, Texas, have released Anaconda, a free package that bundles together around 200 of the most popular Python libraries for science, maths, engineering and data ...

In addition to supporting Python, the company is supporting things like the Juypter data science notebook, the Streamlit framework, and the Bokeh visualization library, Bajuk says.

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.