Solutions Review – How to Choose Data Science Software: Expert Advice from a Tech CEO
Choosing data science software for your organization can be a daunting task. In this article from BigData Review, our CEO Ryohei Fujimaki gives his take on selecting data science software. Before selecting a data science platform, the stakeholders should determine the best uses cases, requirements, and impact, keeping in mind the primary users of your data science application and the programming language that they use.
The rationale for selecting a particular Data Science platform depends on the target user’s preference for customization, flexibility, and automatic discovery of new features. A no-code or low-code approach to data science is an essential consideration in selecting a data science platform. A no-code environment is ideal for BI and analytics teams, which prefer visual tools that leverage drag-and-drop functionality to make the data science process easier for non-data scientists. Head over to the article to read more. @BigData_Review #machinelearning
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dotData Automated Feature Engineering powers our full-cycle data science automation platform to help enterprise organizations accelerate ML and AI projects and deliver more business value by automating the hardest part of the data science and AI process – feature engineering and operationalization. Learn more at dotdata.com, and join us on Twitter and LinkedIn.
