Proprietary solutions, once the mainstay of enterprise data science, are now being eclipsed by open source projects like R, Spark, and TensorFlow. There are several reasons for this trend: Open source tools offer endless opportunities for collaboration and contribution, and many have been built out to the point that they provide very real value, even at an enterprise level. Did you know that 62% of analytics professionals prefer open source languages Python and R to proprietary legacy solution SAS? Open source tools are winning at the enterprise level, but deciding which ones to add to your tech stack is not so straightforward.
To simplify the process, our friends over at DataScience, Inc. developed DataScience Trends, a free tool that makes it easy to explore 2.8 million open source repositories for significant development activity, popularity, and more.
The possibilities are endless. Using DataScience Trends, we see that interest in the neural network library Keras has grown substantially with the release of Google’s TensorFlow. We see that data visualization library ggplot is gaining on matplotlib. You can learn more about these insights and others by downloading the new white paper, “DataScience Trends Report: Open Source Tools for Enterprise Data Science.” The white paper looks at activity data from the most popular GitHub repositories to identify trends in data visualization tools, deep learning libraries, and open source licensing using the interactive DataScience Trends tool.