Applied Social Network Analysis in Python

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representat

Created by: Daniel Romero

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Overall Score : 92 / 100

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Course Description

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.

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Instructor Details

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Daniel Romero is an Assistant Professor with the School of Information at the University of Michigan. His main research interest is in the empirical and theoretical analysis of Social and Information Networks with a particular interest in understanding the mechanisms involved in network evolution, information diffusion, and user interactions on the Web.

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Reviews

4.6

216 total reviews

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By DINESH A Z on 2-Dec-18

Very Nice Coursera! It lead me to reknow the relations among the worrld.

By Vishal S on 16-Jul-18

Lectures are very well-designed. Especially, the assignment of week 4 is too good, that give me an overview of how we can apply machine learning in network analysis.

By Christian E on 27-Mar-19

Very new on this topic and very interesting

By Shwetank A on 27-Jul-19

nice course

By Magdiel B d N A on 12-May-19

ok

I really appreciate that you offer me such a great specialization of courses.Since I've finished the final course eventually, I should offer my gratitude to you.

By Jiahui B on 29-Nov-17

Very useful course. It helps me finish my course project.

By Darren on 24-Sep-17

The best course I had on coursera ever, it really broaden my scope of knowledge. It worth waiting so much time

Another must to have lesson from Michigan Univeristy. After completing this lesson the Social Networks will be an analysis challenge.

By Mile D on 20-Dec-17

Excellent explanations and examples. Recommended text to read was also very helpful. Thanks for providing this course!!!

By LEE D D on 6-Nov-17

Excellent! It was one of the great assignments I ever had!

By James M on 30-May-18

This is the last course of the Applied Data Sci in Python certificate. It effectively ties together all the introduced concepts from the previous courses (except Natural Language Processing). Daniel Romero was an extremely effective lecturer and many of the concepts and know-how were introduced, taught, and assessed appropriately. I'm also impressed that I was able to learn a new python library I (or my coworkers) had not heard of before.