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As artificial intelligence becomes increasingly important to analytics, the data science life cycle must evolve to support greater scalability, efficiency, and accessibility—from data preparation and model building to deployment, communication, and governance. Today’s data teams face growing pressure to deliver faster while managing complex workflows, bridging skill gaps, and maintaining trust and transparency. Communicating results effectively through reports, visualizations, dashboards, applications, and technical documentation is also a critical part of this life cycle. Open-source tools such as R and Python remain at the heart of modern data science, but they can be complex to learn and use.

Join Fern Halper, Ph.D., VP of Research at TDWI, and experts from Posit and Databricks to discuss how AI-infused tools can help transform data science.