Open source tools for analytics have been available for decades, but there has been a recent surge in use as younger analysts and data scientists fashion their own identity and more organizations make the move to analyze big data.
In fact, open source has become quite popular because it is a low-cost source community for innovation which appeals to many data scientists and analytics application developers—especially those who like to code. These toolkits are often used to build predictive analytics/machine learning (PA/ML) models.
As organizations move to build and deploy predictive analytics models, they need to determine which kinds of tools to use—commercial versus open source (or both). Some important advantages and disadvantages of open source for analytics are explored in this report.
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