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About this course

Graph analytics and graph databases are one of the fastest growing areas in data analytics, and machine learning. Companies like Google, UberEats, Pinterest and Twitter, have leveraged graphs to transform their core products. As more enterprises embrace graphs, there is a huge demand for engineers and data scientists with graph analytics skills.

In this training you will learn everything you need to know to get started building sophisticated applications using Python graph algorithm libraries and visualization tools, and graph databases. You will start with basic graph concepts, work your way to graph algorithms, and finish the course by building a graph-based fraud detection application from scratch.

What will you learn

By the end of this course, you will have learned:

  • How to manipulate, analyze, and model graph data.
  • How to work with a powerful Python graph library called NetworkX
  • How to work with various graph visualization packages including Cytoscape, Gephi, and Graphviz
  • About major graph algorithms including Breadth-First Search, Depth-First Search, and Page Rank, and how to apply them to real world examples
  • About how graph analytics can improve your machine learning models
  • How to build a production-ready graph application from scratch using a graph database, Flask, and D3.js

Who is this course for

This course is for Python developers and data scientists of all levels. If you enjoy staying ahead of the curve by learning the latest data technologies, this course is for you!

Course outline

Introduction to Graph Networks

You’ll be introduced to fundamental concepts in graph analytics while exploring real-world datasets. You'll also learn about NetworkX, a library that allows you to manipulate, analyze, and model graph data in Python. We will dive deep into different ways of loading graphs into NetworkX.

Graph Visualization

We’ll cover the basics of visualising and exploring graphs in Python with Matplotlib. We’ll also give an overview of advanced packages for graph visualisation like Cytoscape, Gephi and Graphviz.

Section 2 – Graph algorithms & data STRUCTURES
Graph Algorithm Basics

You’ll be introduced to fundamental concepts in graph traversals, breadth-first search and depth-first search, as well as the basics of path-finding algorithms. You’ll also learn about ways to identify nodes that are important in a network.

Graph Structures

You’ll learn about interesting structures within network data and ways of finding them. We’ll cover essential concepts such as cliques, communities, and subgraphs.

Machine Learning with Graphs

You’ll learn how to bring graph networks and machine learning together by embedding each node of a graph into a real vector. We will also cover representation learning and Graph Neural Network and introduce StellarGraph, a library with algorithms for graph machine learning.

Section 3 – Hands-on Project
Build a Production Application Using Graphs

In this final chapter, we will be bringing everything you learned together to build a production application for detecting credit card fraud in a network of transactions.

Your instructor

Dominik Tomicevic

Co-Founder and CEO at Memgraph

Hey there 👋. I'm Dominik Tomicevic, passionate software engineer and founder and CEO of Memgraph a high-performance graph database company based in London, UK. Passionate about building distributed systems, highly concurrent and lock-free algorithms and data structures. P.S. I love graphs!

Join thousands of Python developers learning graph analytics today!

Sign up for this FREE live training!

Only 100 spots available!

We will never share your information.