Information Technology
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Introduction to Network Analysis in Python

Course Cover

5

(3)

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Beginner

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Networks are everywhere. They can be found on social media platforms like Facebook, Twitter, and even transportation networks such as bike-sharing networks. Data scientists will be able to open up new opportunities by learning how to analyze them. This course will show you how to analyze, visualize, and make sense of networks. NetworkX will allow you to apply these concepts to real-world data. With the knowledge gained in this course, you'll be able see your data differently and will improve your network thinking skills.

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Highlights

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Hands on training

Top 10 Percentile

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Rating & Reviews

Top 30 Percentile

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Hands on training

This course stands out as one of the top 10 percentile options in Python Programming, offering unparalleled hands-on training. Learners gain practical experience and skills through immersive learning, preparing them for real-world challenges. It ensures a well-rounded skill set, catering to a range of learning preferences. With a focus on Hands on training as well as essential Case Based Learning and Virtual Labs, this course is tailored to meet diverse educational needs.

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Rating & Reviews

This highly acclaimed course is among the top-rated in Python Programming, boasting a rating greater than 4 and an overall rating of 5.0. Its exceptional quality sets it apart, making it an excellent choice for individuals seeking top-notch learning experience in Python Programming.

Course Overview

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Virtual Labs

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International Faculty

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Case Based Learning

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Post Course Interactions

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

Skills You Will Gain

Prerequisites/Requirements

Python Data Science Toolbox (Part 2)

What You Will Learn

This course will equip you with the skills to analyze, visualize, and make sense of networks

You'll apply the concepts you learn to real-world network data using the powerful NetworkX library

Course Instructors

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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...

Course Reviews

Average Rating Based on 3 reviews

5.0

100%

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