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Applied Social Network Analysis in Python

University of Michigan via Coursera

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  • Provider Coursera
  • Subject Data Science
  • $ Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 4 weeks long
  • Learn more about MOOCs

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Overview

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This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem.

This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

Taught by

Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero and V. G. Vinod Vydiswaran

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Reviews for Coursera's Applied Social Network Analysis in Python
4.5 Based on 2 reviews

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  • 1
Ronny W
5.0 a year ago
by Ronny completed this course, spending 6 hours a week on it and found the course difficulty to be medium.
Well structured course covering social network concepts, explaining the main features of networks and its nodes and edges. The algorithms are well explained, nicely illustrated and demoed with jupyter notebooks. Weekly quizzes check your understanding of the concepts and the assignments let you apply the material on practical examples, from basic network properties to link prediction using machine learning.

After finishing this course you are familiar with the python networkx library and ready to explore and analyze social networks on your own.

This is the final course of a specialization, ensure you have the necessary prerequisite skills or follow the earlier courses in the specialization first.
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Raivis J
4.0 5 months ago
Raivis completed this course, spending 4 hours a week on it and found the course difficulty to be easy.
Interesting topic, but quizzes again suffer from too much theoretical questions. Final programming assignment was very easy, you can re-use the code written in the final assignment of Machine Learning course in this specialisation (but that does not mean it's a bad thing).
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