Description
Course info
Rating
(15)
Level
Beginner
Updated
Apr 10, 2019
Duration
1h 58m
Description

Companies have amassed terabytes of data that can be represented as networks. However, due to a lack of data professionals skilled in network methods, this data is being underutilized. The aim of this course is to fix that and empower you to be able to reason about and build products based on networks. In this course, Network Analysis in Python: Getting Started, you'll gain the foundational skills needed to analyze networks using Python. First, you'll learn about the origins of network science and its relation to graph theory, as well as practical skills in manipulating graphs in NetworkX. Next, you'll explore how to create beautiful and illustrative visualizations of networks using the native capabilities of NetworkX and Bokeh. Then, you'll deep dive into centrality and community detection algorithms. Finally, you'll enrich your machine learning toolbox by learning about network embeddings. By the end of the course, you'll have learned how to conduct your own analysis of networks, how to visualize networks, and even how to build an advanced friendship prediction engine using network science and machine learning.

About the author
About the author

Artur is a Data Scientist helping fight financial crime at Revolut.

Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi. My name is Artur Krochin, and welcome to my course, Network Analysis in Python: Getting Started. I'm an experienced data scientist that has modeled, wrangled, and phish-engineered data from a variety of industries including telecommunications, banking, and images sensory. Over the past years, I have grown quite frustrated with the fact that companies and individual data scientists tend to disregard network science. The aim of this course is to fix this situation and make you more aware of network science techniques. As a data professional, this course is all about practical network science. You will learn algorithms that underpin the operations of such technology giants as Facebook and Google. In this course, we're going to learn NetworkX and the basic principles of graph theory and network analysis. Next, we'll move on to visual network analysis. We'll develop a muscle for reading networks and spotting irregularities and patterns. And then we'll cover some of the core concepts of network science where we'll discuss how centrality and community detection algorithms work. The course finishes off with a module on machine learning. By the end of this course, your toolbox as a data professional will be significantly enriched, and you will be comfortable in dealing with network data. Before beginning this course, you should be familiar with the basics of Python. I hope you'll join me on this journey to learn the fascinating field of network science with the Network Analysis in Python: Getting Started course, here at Pluralsight.