Graph data science: What you need to know
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 we’ve 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 30-second explanation, according to Alicia Frame.
Frame is the senior director of product management for data science at Neo4j, a leading graph database vendor. She has a doctorate in computational biology, and has spent 10 years as a practicing data scientist working with connected data.
When she joined Neo4j about three years ago, she set out to build a best-in-class solution for dealing with connected data for data scientists. Today, the product Frame is leading at Neo4j, aptly called Graph Data Science, is celebrating its two-year anniversary with version 2.0, which brings some important advancements: new features, a native Python client and availability as a managed service under the name AuraDS on Google Cloud.