It took just over a decade (give or take) for data science and machine learning to grow from ancillary functions within businesses into major lucrative industries in their own right. Still, many of the tools that data professionals use every day have retained a grassroots, community-driven approach, and the ecosystem as a whole has preserved a fondness for freely shared open-source software.
This week, let’s explore the intersection of data science and open-source culture. We’ve selected a handful of recent articles that celebrate this relationship and center projects and products that eschew tech’s tendency to focus on walled gardens and bottom lines. Let’s dive in.
- The whys, hows, and whats of contributing to OS projects. As a prolific contributor, Maarten Grootendorst has a unique perspective on the benefits data scientists can reap by joining open-source projects. His experience has also helped him develop a pragmatic approach to the challenges of less-structured (and sometimes all-out chaotic) development workflows.
- A practical assessment of an emerging programming language. Python and R fans have been debating the languages’ respective strengths for a very long time, but in recent years Julia, which is backed by a strong community of developers, has emerged as one of data scientists’ most popular alternatives to the Big Two. Natassha Selvaraj walks us through the factors that make Julia a compelling contender, and offers a beginner-friendly introduction in case you’d like to get started with the basics.
The Open-Source Spirit of Data Science Republished from Source https://towardsdatascience.com/the-open-source-spirit-of-data-science-18fa1cb9d3c1?source=rss—-7f60cf5620c9—4 via https://towardsdatascience.com/feed