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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來自APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON的熱門評論
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.
It was an easy introductory course that is well structured and well explained. Took me roughly a weekend and I thoroughly enjoyed it. Hope the professor follows up with more advanced material.
Excellent tour through the basic terminology and key metrics of Graphs, with a lot of help from the networkX library that simplifies many, otherwise tough, tasks, calculations and processes.
I loved this course. It was well taught and had excellent problem sets and quizzes to internalize the learning. The material is very relevant to the market today. I highly recommend it.
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.
The material and assignments were great and well aligned. The autograder for the Jupyter Notebooks was finicky at best and resulted in lots of time wasted getting formatting correct.
Interesting material and easy to follow. Assignments and quizzes were sufficiently challenging, but not too difficult that I spent entire weekends troubleshooting my code.
This was an excellent overview of using and analyzing graphs with Python. I learned a lot, got to apply my learning from previous courses, and I earned my Specialization!
Very good class.\n\nThe lecturer is amazing!! The quizzes help you understand the concepts. The assignments are a little basic though.\n\nOverall you learn a great deal.
It's rare to find an amazing course in network analysis online, and I'm very glad to have taken this course and learn the art of network analysis for research purposes.
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.
Great class for an introduction to networks.I didn't give it 5 stars because it didn't give me enough information to apply the concepts learned to real life projects.
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.
Very comprehensive course for introduction of social network analysis. Best part is every concept is covered in detail and how to implement using networkx library.
Course was ok, the assignments are not too difficult. I wish the course would provided more insights and discussions of the presented metrics of centrality though.
I found it hard sometimes to understand the concepts but this gave me quite an introduction on social network analysis and encouraged me to learn more about them.
Great explanations. The instructor is awesome and has good visual material. In-video quizzes keep you engaged during the lecture. I am very happy with the course.
Really helpful. Get a basic idea of the social network and how to use python to analyze it. Will definitely dig deeper and see how it could relate to my work .
Overall, good course. It could use more explicit examples of NetworkX in the actual Jupyter Notebook itself, but the coverage of the material is high quality.
All over the course is very relevant to what is a need in industry. Very nice video lectures, to the point and crisp. Material is quite informative too.
關於 借助 Python 应用数据科学 專項課程