Graph Engineering and Graph Data Scientist Roadmap Repository

Welcome to our dedicated repository for graph engineering and graph data scientist roadmaps! This repository is an ongoing project aimed at providing comprehensive guidance for individuals interested in pursuing careers or enhancing their skills in these dynamic fields. While the content will be continuously updated to reflect changes in the field, the fundamentals will remain consistent to ensure a strong foundation for learners.

We have developed two distinct roadmaps, each with its unique focus, yet converging on the overarching goal of leveraging graph data for solving real-world problems:

1. Graph Engineering: A graph engineer possesses a combination of software engineering and data engineering skills, along with a deep understanding of graph data. This includes proficiency in graph data modeling, knowledge graph and experience working with graph databases. The roadmap for graph engineering will cover essential topics such as graph theory, data structures, graph databases, query languages, and best practices for designing and implementing graph-based solutions.

This roadmap aims to give a complete picture of the graph data engineering landscape and serve as a study guide for aspiring graph engineers.

Note to beginners

In your journey you shouldn't feel overwhelmed by the numble of concept and framework listed here. As a graph engineer you are expected to master a few tools for several years depending on your organization requirements and interest. Example, if your company doesn't work with RDF, but instead LPG. You should master LPGs

2. Graph Data Scientist: Graph data scientists are individuals equipped with expertise in data science and analytics, who utilize graph data science algorithms to derive insights and solutions for business use cases. The roadmap for graph data scientists will encompass foundational concepts in data science, machine learning, and statistics, along with specialized techniques for analyzing and interpreting graph data. Topics such as graph algorithms, centrality measures, community detection, and graph embedding techniques will be covered in detail.

While these roadmaps have distinct focuses, they also converge at various points, emphasizing the importance of interdisciplinary skills and collaboration in the field of graph technology. Whether you aspire to become a graph engineer, a graph data scientist, or a hybrid professional combining aspects of both roles, our repository aims to provide you with the resources and guidance needed to succeed.

We encourage you to explore the roadmaps, engage with the content, and contribute to the ongoing evolution of this repository. Together, let's advance our understanding and utilization of graph data to tackle complex challenges and drive innovation across industries.

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Contributors

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