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
Quality Score
Overall Score : 92 / 100
Course Description
Instructor Details
- 4.6 Rating
216 Reviews
Daniel Romero
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
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 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
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