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.
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Université du Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
- 5 stars74,03 %
- 4 stars19,99 %
- 3 stars4,05 %
- 2 stars0,99 %
- 1 star0,91 %
Meilleurs avis pour APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON
This course contains many important concepts of Graph Theory and Network Analysis. The explanation is clear and neat. Also, the assignments are fun and comprehensible.
Really enjoyed the mathematical component of this course. It was fun to see how you could connect the graph theoretical components to the machine learning concepts from earlier courses.
Great content but assignment / auto grader sometimes difficult to deal with. In particular, errors not clearly described. Much time wasted due to wrong package version, etc. etc.
This course is a excellent introduction to social network analysis. Learnt a lot about how social network works. Anyone learning Machine Learning and AI should definitely take this course. It's good.
À propos du Spécialisation Science des données appliquée avec 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.
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