Graph data science is an emerging field with a lot of promise, but it’s being hamstrung by the need for practitioners to have lots of data engineering and ETL skills. Now Neo4j is hoping to drive that ...

Neo4j Graph Data Science makes it easy for data scientists to work within their existing data pipeline of tools across their ecosystem. Data scientists can use Neo4j Graph Data Science on-premises, ...

Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points — that’s the 30-second explanation, according to Alicia Frame.

Data science and machine learning features: Notebooks and Graph Neural Networks GQL still has some way to go. Standardization efforts are always complicated , and adoption is not guaranteed across ...

Data visualizations are some of the most powerful tools in a climate science communicator’s playbook. The most famous have ...

The application of graph processing and graph DBMSs will grow at 100 percent annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science ...

In data set 4, almost everybody sleeps exactly eight hours per day, and their sleep habits have no correlation to how much they exercise, whereas one person in the sample sleeps almost 20 hours a ...

According to Tracey L. Weissgerber, Natasa M. Milic, Stacey J. Winham, and Vesna D. Garovic, proper representation of small data sets and sample sizes allows accurate interpretation. Doing so, they ...

Continuous intelligence apps always have to have an answer, so analysis must be continuous because data streams are boundless, and most real-world data is only ephemerally useful.