Enhance and accelerate data predictions with the Neo4j Graph Data Science Library, a powerful data structure for global computations with robust algorithms.
Neo4j created the first enterprise graph framework for data scientists to improve predictions that drive better decisions and innovation. Neo4j for Graph Data Science incorporates the predictive power of relationships and network structures in existing data to answer previously intractable questions and increase prediction accuracy.
The Neo4j Graph Data Science™ Library is the analytics engine of this framework, making it possible to address complex questions about system dynamics and group behavior. Data scientists benefit from a customized, flexible data structure for global computations and a repository of powerful, robust algorithms to quickly compute results over tens of billions of nodes.
Graph algorithms provide unsupervised machine learning methods and heuristics that learn and describe the topology of your graph. The GDS™ Library includes hardened graph algorithms with enterprise features, like deterministic seeding for consistent results. And with graph embeddings and trained models inside of the analytics workspace, you can make predictions about your graph from within Neo4j.
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The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
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A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.