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Price: $189

Regular price $210, discounted 10%

  • 4 hour immersive session

  • Hands-on training with Q&A

  • Recording available on-demand

  • Certification of Completion

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Subscribe and get an additional 10% to 35% off ALL live training session

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Meet Your Instructor

Noemi Derzsy, PhD

Noemi Derzsy is a Senior Inventive Scientist at AT&T Chief Data Office within the Data Science and AI Research organization. Her research is centered on understanding and modeling customer behavior and experience through large-scale consumer and network data, using machine learning, network analysis/modeling, spatio-temporal mining, text mining and natural language processing techniques. 
Prior to joining AT&T, Noemi was a Data Science Fellow at Insight Data Science NYC and a postdoctoral research associate at Social Cognitive Networks Academic Research Center at Rensselaer Polytechnic Institute. She holds a PhD in Physics, MS in Computational Physics, and has a research background in Network Science and Computer Science.
Noemi is also involved in volunteering in the data science community. She is a NASA Datanaut and former organizer of Data Umbrella meetup group and NYC Women in Machine Learning and Data Science meetup group.

Course Overview

Networks, also known as graphs are one of the most crucial data structures in our increasingly intertwined world. Social friendship networks, the world-wide-web, financial systems, infrastructure (power grid, streets), etc. are all network structures. Knowing how to analyze the underlying network topology of interconnected systems can provide an invaluable skill in anyone’s toolbox. This tutorial will provide a hands-on guide on how to approach a network analysis project from scratch and end-to-end: how to generate, manipulate, analyze and visualize graph structures that will help you gain insight about relationships between elements in your data. You will learn how to detect communities in network to identify more densely interconnected subgroups used on social media platforms to detect social groups, and how to most effectively highlight them in a graph visualization. Analyzing a network from real data is crucial in understanding the patterns and behaviors of a real system. But often times you will need to build synthetic networks, which can serve as baseline models for your studies, or sometimes it becomes even more cost efficient to rely on synthetic networks instead of collecting large-scale data. In the last part of this course you will learn when, why and how to build synthetic networks.


Why Enroll?

By the end of the course, participants will be able to:

  • Understand the basics of graphs/networks properties and analysis, including what can you use it for and how

  • Learn how to generate basic network types, and the most often encountered network models in real data. Next, discover the most informative network measures to understand network structures and behaviors

  • Extract and interpret information about real public social network data by building, analyzing and visualizing it to gain understanding about its structure and behaviors.

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Course Outline

Module 1: Network/Graph Science Overview (30 min)

●  Training Overview
● A Brief History from Graph Theory to Network Science
● Real-World Applications of Networks/Graphs Overview
● Basic Network Structural Properties
● Graphs in Python with NetworkX

Module 2: Generate & manipulate graph structures (30 min)

● Create, modify and delete graphs
● Node, edge properties, and structure
● Create graph structure from datafile
● Weighted graphs
● Directed graphs
● Multigraphs
●  Bipartite graphs

Module 3: Analyze networks (45 min)

● Structural properties analysis
● Node degree, average degree, degree distribution
● Clustering, coefficient, triangles
● Paths, diameter
● Centrality measures
● Components
●   Assortativity

Module 4: Visualize networks (15 min)

●  Network visualization with NetworkX
● Network visualization with nxviz
● Visualize subgraphs
● Network visualization with node attributes

Module 5: Community detection (60 min)

● Community detection algorithms overview
● Community detection best practices
● Identify communities in a real social network
● Visualize communities in a network

Module 6: Network models (60 min)

● Network models overview
● Build synthetic networks from various network models
● Compare synthetic network and real network topological properties

Real-world use-cases
  • Network analysis and modeling is used by online social media companies (i.e. Facebook, Twitter) to study opinion formation and influencing in social networks. Graph-based methods are also used to suggest new contacts on the platform or to recommend new products to customers, based on the products their online friends are interested in.

  • Contact tracing skills and the ability to analyze and model infectious disease spreads, are all essential applications of networks/graph, especially during the COVID-19 pandemic.

  • Graph-based analysis and modeling are crucial in solving transportation system optimization problems, such as optimizing power-grid systems, airline or ground traffic flow and determine shortest paths, the most cost-efficient routes between destinations (i.e. Google Maps).

  • Linguists and language enthusiasts: If you don’t think you have the “technical background” of Python or machine learning, you’ll be able to quickly level up. This is for you, too!

Key Details





AUGUST 3RD, 2021





Basic Python, Jupyter Notebooks, and installation of NetworkX package.

Upcoming Live Training

August 17th

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Bayesian methods are powerful tools for using data to answer questions and guide decision making under uncertainty. This workshop introduces PyMC, which is a Python library for Bayesian inference. We will use PyMC to estimate proportions and rates, and use those estimates to generate predictions. These methods have applications in business, science, and engineering.

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