BLENDED LIVE TRAINING: September 30th

1 PM EST

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Price: $189

Regular price $210, discounted 10%

  • 4 hour immersive on-demand session with live exercises and discussion time

  • 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

Eric Ma

Eric is a Principal Data Scientist at Moderna supporting research data science. Prior to Moderna, he was at the Novartis Institutes for Biomedical Research conducting biomedical data science research with a focus on using Bayesian statistical methods in the service of making medicines for patients. Prior to Novartis, he was an Insight Health Data Fellow in the summer of 2017 and defended his doctoral thesis in the Department of Biological Engineering at MIT in the spring of 2017.

Eric is also an open-source software developer and has led the development of pyjanitor, a clean API for cleaning data in Python, and nxviz, a visualization package for NetworkX. In addition, he gives back to the open-source community through code contributions to multiple projects.

His personal life motto is found in the Gospel of Luke 12:48.

Course Overview

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 tutorial is for you. In this tutorial, 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.

Why Enroll?

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

  • Use the NetworkX package and the Python programming language to manipulate and visualize graphs

  • Understand how graph algorithms work, particularly how to “think on” graphs

  • Use linear algebra to represent graph problems and speed them up

  • Load graph data to and from disk.

Learn the fundamentals of network thinking from Eric Ma
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Course Outline

Part 1: Introduction

  • Networks of all kinds: biological, transportation
  • Representation of networks, NetworkX data structures
  • Basic quick-and-dirty visualizations

Part 2: Hubs and Paths

  • Finding important nodes; applications
  • Pathfinding algorithms and their applications
  • Hands-on: implementing path-finding algorithms
  • Visualize degree and betweenness centrality distributions

Part 3: Cliques, Triangles & Structures

  • Definition of cliques
  • Triangles as the simplest complex clique, applications
  • Using path-finding algorithms to find structures in a graph
  • Open triangles as recommender systems.

Part 4: Bipartite Graphs

  • Definition of bipartite graphs, applications
  • Constructing bipartite graphs in NetworkX
  • Summary statistics of bipartite graphs

Part 5: Linear Algebra and Graphs

  • Graphs as matrices: adjacency and node feature matrices
  • Message passing operations and how it is used in graph deep learning
  • Speed vs. code readability tradeoffs when using matrix operations

Real-world use-cases

  • Recommender systems: Using graph structures to recommend products or professional connections.

  • Epidemiological analysis: Figure out the most important spreaders of a disease.

  • Logistics:Identify the most efficient path to move goods and services.

Key Details

DATE

TIME:

DURATION:

LEVEL:

SEPTEMBER 30TH, 2021

TIME: 1 PM EST, 10 AM PST

4 HOURS

INTERMEDIATE 

Prerequisites

If you’re familiar with the Jupyter notebook/lab interface, are comfortable with Python programming (loops, functions, conditionals), and know how to make plots in matplotlib, you’ll be well-prepared for the tutorial!

Upcoming Live Training

October 6th

NLP Fundamentals

In this live training session you will understand NLP from first principles, progressing from basic fundamentals to state-of-the-art NLP. You will also use three popular modern open source NLP libraries (spaCy, fast.ai, and Hugging Face) to build NLP applications in this 3-hour, hands-on session.

learn more
Open Data Science

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