Hands On Training

Intermediate Network Analysis in Python

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

DataCamp's Introduction to Network Analysis With Python is completed and you want to learn more advanced techniques to analyze social, transport, and biological networks. This course is for you! This course will allow you to increase your network analytics skills and knowledge. This course will teach you how to use inverse and bipartite graphs to analyze changing time series within networks. Data Science is all about graph projections. Learn how to store and load graph data from files. The final chapter will consolidate all of this knowledge. This chapter will teach you how to analyze forum data and become a Pythonista Network Analysis Ninja.

Course Overview

Virtual Labs

International Faculty

Case Based Learning

Post Course Interactions

Case Studies,Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Introduction to Network Analysis in Python

What You Will Learn

Herein, you'll build on your knowledge and skills to tackle more advanced problems in network analytics!

You'll also learn about graph projections, why they're so useful in Data Science, and figure out the best ways to store and load graph data from files

You'll consolidate all of this knowledge in a final chapter case study, in which you'll analyze a forum dataset and come out of this course a Pythonista Network Analyst ninja!

You'll gain the conceptual and practical skills to analyze evolving time series of networks, learn about bipartite graphs, and how to use bipartite graphs in product recommendation systems

Course Content

Expand all sections

Module 1: Bipartite graphs product recommendation systems

Module 2: Graph projections

Module 3: Comparing graphs time-dynamic graphs

Module 4: Tying it up!

Course Instructors

Eric Ma

Data Carpentry instructor and author of nxviz package

Eric uses code to solve big biological data problems at MIT. His tools of choice are: deep learning, network analysis, non-parametric and Bayesian statistics. He has domain expertise in the life scie...