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
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.
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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