This course covers data analytics and machine learning (ML) with graph data structures, focusing on node, edge, and graph embedding, and representation learning. It includes descriptive network analysis, traditional ML on graphs, and graph neural networks. Practical aspects of the course involve coding for network analysis and ML. The course also features contemporary topic presentations by students and a hands-on project, blending theory with real-world application.