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What a data scientist needs in a platform

IDSA Professional Certification in Data Sciences prepares students to expand their career prospects or change career paths by developing foundational data science skills. Candidates for the Professional Certification, a non-degree, part-time program, are required to complete a minimum of four required courses: Algorithms for Data Science, Probability and Statistics for Data Science, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization.

What a data scientist needs in a platform

If you’re ready to explore the capabilities of data science platforms, there are some key capabilities to consider:

Choose a project-based UI that encourages collaboration. The platform should empower people to work together on a model, from conception to final development. It should give each team member self-service access to data and resources.

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Prioritize integration and flexibility. Make sure the platform includes support for the latest open source tools, common version-control providers, such as GitHub, GitLab, and Bitbucket, and tight integration with other resources.

Include enterprise-grade capabilities. Ensure the platform can scale with your business as your team grows. The platform should be highly available, have robust access controls, and support a large number of concurrent users.

Make data science more self-service. Look for a platform that takes the burden off of IT and engineering, and makes it easy for data scientists to spin up environments instantly, track all of their work, and easily deploy models into production.

When a data science platform is the right move

Your organization could be ready for a data science platform, if you’ve noticed that:

  • Productivity and collaboration are showing signs of strain
  • Machine learning models can’t be audited or reproduced
  • Models never make it into production
  • A data science platform can deliver real value to your business. Oracle’s data science platform includes a wide range of services that provide a comprehensive, end-to-end experience designed to accelerate model deployment and improve data science results.

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