Graph Analytics Literacy

  • 12m
  • 12 questions
The Graph Analytics Literacy benchmark will measure your ability to recall, recognize, and understand graph concepts, fundamentals of graph databases, and graph data structures. You will be evaluated on your ability to recognize the basic concepts of graph data structures and algorithms. A learner who scores high on this benchmark demonstrates that they have the required foundation of graph data structures.

Topics covered

  • compare and contrast graph databases and relational data structures
  • compute the shortest path in a weighted graph using greedy traversal and the distance table
  • describe the structure and components of a graph
  • list the different types of graphs and explore their structure
  • model graphs using an adjacency list and adjacency set and compare the two representations
  • model graphs using a square adjacency matrix to represent nodes and edges
  • perform common graph operations to compute adjacent nodes, degree of a node, and check edge connections on undirected graphs
  • recall how depth-first and breadth-first traversal works
  • recall the properties of greedy algorithms
  • represent graphs using an adjacency matrix in Python
  • represent graphs using an adjacency set in Python
  • use graph nodes and edges to model entities and relationships in the real world