Course Abstract

Training duration : 90 minutes

Have you ever wondered about how those data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this course is for you. In this course, we will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, and visualizing complex networks.

DIFFICULTY LEVEL: BEGINNER

Learning Objectives

  • Familiarity with how to use the NetworkX and nxviz Python packages for modelling and rationally visualizing networks

  • Be able to load node and edge data from a Pandas dataframe

  • Familiarity with object-oriented and matrix-oriented representations of graphs

  • Be able to find paths between nodes, interesting structures in graphs, and projections of bipartite graphs

  • Be able to use matrix operations to simulate diffusion of information on networks

Instructor

Instructor Bio:

Eric Ma, PhD

Author of nxviz Package

Course Outline

Coming Soon

Background knowledge

  • This course is for current and aspiring Data Scientists and Data Visualization & Network Theory enthusiasts

  • Knowledge of following tools and concepts is useful:

  • Learners should have a grasp of Python programming

  • Loops and basic Python data structures

Real-world applications

  • Network analysis tools are actively used by data science teams at Facebook and Linked for network recommendations.

  • Social network analysis help detect hate-speech based on activities on Twitter social-network

  • Other use-cases of networking analytics includes fraud detection, customer churn prevention and cloud security