In this graph analytics for Python developers training, you learn everything you need to know to build a production-ready graph-based Python application.
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.
By the end of this course, you will have learned:
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!
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.
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.
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.
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.
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.
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.
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!