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Вернуться к Applied Social Network Analysis in Python

Отзывы учащихся о курсе Applied Social Network Analysis in Python от партнера Мичиганский университет

Оценки: 2,571

О курсе

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....

Лучшие рецензии


2 мая 2019 г.

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.


23 сент. 2018 г.

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.

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51–75 из 427 отзывов о курсе Applied Social Network Analysis in Python

автор: 王玉龙

18 окт. 2017 г.

Eventhough the tutorial video is also switch to the teacher's face that make me stop the video to see the slide frame.But It's intuitive to understand the basic concept about the network with some exercise to enforce the knowledge. The final exercise is more intersting...

автор: Praveen R

10 дек. 2019 г.

I learnt about networkx and its capabilities. The course introduces to many network algorithms and talks about concepts of centrality, page rank, etc. Good eye opener to all these concepts. The last assignment is very practical and challenging. Enjoyed the course.


автор: Dongliang Z

18 янв. 2018 г.

I enjoyed this course. This course is about the basic knowledge in network analysis. I do hope the lecturer can give more knowledge and application in network analysis. (Perhaps holding a series courses of Network Analysis in Python will be very good in the future!)

автор: Dung D L

14 сент. 2020 г.

Wonderful course with plenty of amazing knowledge about Graph and Network that I have never been approached. After this course, I have several skills to apply to my job. I truly appreciate the teachers, TA, and all people who contributed to this course.

автор: john w

21 апр. 2018 г.

Well put together. Quizzes test on material covered and assignments expand on it. There is still challenge and rigor, but it comes from understanding the concepts, not ambiguity and lack of instruction. This is one of the best online courses I've taken.

автор: Nikolay S

2 янв. 2019 г.

The course and the tutor are great.

I learned how to create and manage network graphs using python with networkx. I was really satisfied from the last week assignment when I had to work with real-life example plus machine learning classifier.

автор: sampath A B

2 дек. 2020 г.

I have really enjoyed the course ("Applied Social Network Analysis in Python."I like the way you summarize each module at the end of the module. I think others should learn from you.However, the python "Networkx" library is very annoying.

автор: Juan C E

11 нояб. 2017 г.

Excellent course. Very clear explanations and materials. The assignments were not as difficult as in other courses of the specialization, and very helpful to understand the contents. I highly recommend this course and the specialization.

автор: CHEN S H

26 февр. 2021 г.

The course is very well designed and I learned a lot from it. The quizzes and assignments tested my knowledge. It was also good that the forum tried not to give too many hints to the users so that they can go figure it out themselves.

автор: Ari W R

1 сент. 2020 г.

It is a little bit harder to finishing this course, but i really enjoy it. There're many useful things that we can get from it. I hope always remind this experience about this knowledge and can implemented in the future. Thank you!

автор: Manuel A

22 авг. 2018 г.

Very challenging and comprehensive course, also directly applicable to machine learning problems, as an example, the last assignment applies network knowledge to extract features and exploit them in predictive modelling problems

автор: Alexander G

5 февр. 2019 г.

I got a bit the wrong impression from the title, but it was throughout the course very interesting to learn about Graphs. A welcome addition to the course would be a cheat sheet with the most important quantities.

автор: Ling G

20 сент. 2017 г.

I like this class because the topic is interesting and the homework is not too hard but walks me through some important functionalities of NetworkX. The instructor is also pretty good at presentation as well.

автор: Kedar J

16 нояб. 2018 г.

Great intro course to graph theory and graph analysis using applied python networkx library. The course covers a number of theoretical topics. Would recommend using a local notebook along with the lectures.

автор: Leonid I

18 окт. 2018 г.

Great course! Only one note: the online notebooks use an old version of networkx (v1.11), which is incompatible with the newer v2.2. Therefore, some trickery is required to read pickled networks locally...

автор: Yaron K

21 сент. 2017 г.

Excellent course. Lecturer clearly explains network analysis terms and algorithms with examples, and then shows how they are implemented by the Python networkX library. The assignments exercise their use.

автор: João R W S

6 окт. 2017 г.

Very good course! I've learned a lot both in theory and practical aspects. The final assignment worth to put all together with the skills learned in the other 4 courses of the specialization. Great job!

автор: Nitin K

3 мая 2019 г.

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.

автор: dan s

25 февр. 2018 г.

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.

автор: Machiraju S

18 нояб. 2020 г.

I have never imagined such detailed analysis can be done on a network, nx in python is really powerful package with so many powerful functions that can do ample of analysis at a whim.

автор: Francis J A

23 нояб. 2020 г.

Great introductory course on graph theory using Networkx. The instructor goes through each algorithm with step-by-step examples, and gives relevant examples at the end of each topic.

автор: Spencer R

28 мар. 2020 г.

Very helpful courses. I was able to review and got much better at some things I already knew like data visualization and was able to explore some new areas like network analysis.

автор: Luiz H S

23 авг. 2020 г.

Basic yet informative course. The videos are well paced and the presenter is instructive. The exercises are well made, putting more enphasis on what was learned in the videos.

автор: Nick P

7 окт. 2017 г.

Interesting material and easy to follow. Assignments and quizzes were sufficiently challenging, but not too difficult that I spent entire weekends troubleshooting my code.

автор: Korkrid A

27 сент. 2017 г.

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.