Four-Project Series

- prerequisites
- basic Python • basic SQL • familiarity with tables as CSV files • basic data science and machine learning
- skills learned
- defining graphs • visualizing graphs • working with LynxKite for efficient graph data science • applying linear regression with graph invariants as input features • using graph neural networks

filed under

In this series of liveProjects, you’ll learn to apply insightful graph data science techniques to real-world data problems. Making use of Python and the LynxKite graph data science platform, you’ll explore how graph data structuring can reveal new insights from highly interlinked data. Each liveProject in this series stands alone, so you can pick and choose the skills that are most relevant to you.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

Project 1 Analyze the Graph Structure of Soccer

In this liveProject, you’ll construct event sequence graphs to reveal interesting information about soccer games. You’ll work to find longest pass sequences, most important players, and to understand the spatial structure of the game. You’ll define and visualize these graphs, and use connected components to find interesting event subsequences. You’ll quickly be able to uncover insights such as the most important players and the spatial structuring of the playing pitch.

$29.99
FREE

try now
Project 2 Analyze and Cluster OpenStreetMap Data

Project 3 Optimize City Infrastructure

Project 4 Predict Age Using GNNs

This liveProject is for data scientists interested in the basic techniques of graph data science. This project is suitable for a large range of expertise levels from beginners to experienced practitioners. To begin this liveProject, you will need to be familiar with the following:

- Basic SQL
- Tables, their representation as CSV files
- Basic data science and machine learning

In this liveProject, you’ll learn the basics of graph data structures and how to define relationships between data.

- Defining graphs based on event sequences
- Defining a pass graph among players
- Defining a graph on areas of the pitch
- Various ways of visualizing these graphs
- Using connected components to find interesting event subsequences
- Applying pagerank and reverse pagerank to gain different importance metrics for players
- Working with LynxKite for efficient graph data science

- Self-paced
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- book resources
- Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

RECENTLY VIEWED