Graph data science is moving one step closer to the mainstream: Neo4j releases v2.0 of its eponymous product

Whether you’re genuinely interested in getting insights and solving problems using data, or just attracted by what has been called “the most promising career” by LinkedIn and the “best job in America” by Glassdoor, chances are you’re familiar with data science. But what about graph data science?

As elaborated previously, graphs are a universal data structure with manifestations that span a wide spectrum: from analytics to databases, and from knowledge management to data science, machine learning and even hardware.

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 the 30 second explanation according to Neo4j’s Alicia Frame.

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