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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 reviews in total

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By Steven G on 10-Nov-19

Excellent course. Interesting content and well taught.

By Suleman k on 9-Nov-19

Great for Beginners

By Shiomar S C on 5-Nov-19

Excelente course, the instructor really meks you undestand with the right structure and having meaningfull in video quizes

By Dongquan S on 22-Oct-19

Very well organized course. Thank you!

By KRISHNASAI R on 21-Oct-19

the very best course it is very helpful and useful

By Chethan S L on 2-Oct-19

Excellent

By Amila R on 30-Sep-19

Good starting point for those who want ro learn social network analysis.

By Renzo B on 24-Sep-19

I learned a lot of things that I can apply to my line of work.

By Tian L on 25-Aug-19

a great introductory course.

By Igor K on 23-Aug-19

Nice course, worth to listen to

By Saurabh M on 21-Aug-19

An excellent course

By Kevin c on 14-Aug-19

For a coding heavy course, why doesn't the instructor just upload the code used in slides as a Jupyter Notebook? This would save A LOT OF TIME and frustration. Right now, I have to pause the video to copy the code AND write my own notes and it wastes so much time. Not to mention, you can easily be prone to writing wrong syntax when you're trying to keep up so fast, and then you run the code chunk and it doesn't work and you have to go back to that point in the video. It's a simple staple that I would have expected in a UMich course. Also, they don't show how to create networks from pre-existing data, which is how you will usually work in the real-world

Showing 12 of 216 reviews