An overview of graph methods in data science

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Python is the swiss knife of modern programming languages and a prime candidate to be also the swiss knife for risk modelling

Summary

This course is a CrashProgram (short course) introducing the different ways in which the graph concept enters various data science applications. The course is at an introductory technical level. It requires comfort with mathematical notation and (optionally) ability to run the provided python code with various graph examples. Reviewing the very diverse uses of graph structures will reveal the richness of graphs as data science tools.

  • Abstract Graphs
  • Function Graphs (Charts) (matplotlib)
  • Computation Graphs (tensorflow)
  • Property Graphs
  • Knowledge Graphs (rdflib)
  • Graph Data Structures (In-memory) (networkx, BGL, Lemon, STINGER)
  • Graph Databases (neo4j, arangodb)
  • Data Query Graphs (GraphQL)
  • Probabilistic Graph Models (pgmpy)

Course Level and Type

Introductory Level Core Level Advanced Level
Non-Technical
Technical DAT31050

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