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學生對 密歇根大学 提供的 Applied Social Network Analysis in Python 的評價和反饋

1,462 個評分
237 個審閱


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....



May 03, 2019

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.


Sep 24, 2018

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.


51 - Applied Social Network Analysis in Python 的 75 個評論(共 229 個)

創建者 Rahul S

Oct 08, 2018

Remarkably good explanations, and interesting selection of subtopics. Interestingly , it does not delve into Facebook or any other social media applications, and is still just as valuable as it covers Graphs in some depth. Uses Python and its NetworkX library. Knowledge of classification models and scikit-learn is needed for the 4th assignment.

創建者 Mohammad H

Oct 26, 2018

the course will teach basic of SNA so clear

創建者 Armand L

Oct 28, 2018

Hard but instructive course !

創建者 Thaweedet

Aug 16, 2018

Great, You will to learn how to develop feature for social network data

創建者 Tam

Aug 18, 2018


創建者 Arnab

Nov 03, 2017


創建者 Vladimir

Dec 29, 2017

A very good course to learn about networks. Thanks!

The cherry on top was to apply machine learning techniques to predict how the net evolves.

創建者 Anand T

Jul 07, 2018

A bit intense, bu rewarding

創建者 Oscar J O R

Oct 15, 2017

A really good course. Notebooks could be very useful to practice and maybe more exercises(not graded) with real data.

創建者 Syed A u R

Oct 08, 2017

Excellent course!

創建者 Sara E E

Feb 02, 2018

very practical

創建者 João R W S

Oct 07, 2017

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!

創建者 Elias

Jan 11, 2018

This is a very informative course in the property of networks and the feature extraction you can obtain out of this. Excellent

創建者 Hemalatha N

Dec 08, 2017

Good intro to using networkx

創建者 Ling G

Sep 21, 2017

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.

創建者 Tongsu P

Mar 05, 2018

Very interesting course!

創建者 Servio P

Nov 18, 2017

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.

創建者 J W

Apr 21, 2018

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.

創建者 Emil K

Mar 01, 2018

So, I passed all modules in the whole specialization and received the certificate. This is by far the best course, and the reason for this is the instructor. Daniel Romero is great at explaining the concepts, expresses himself clearly and uses lots of examples which help immensely. The programming assignments are actually fun to solve - the instructions are clear and well-formulated. I know what is expected and can focus on doing data science. For the first time I didn't have to spend hours reading the Discussion Group posts in despair, in order to figure out how to pass the assignments (tricks, hacks, etc). This can't be said about assignments in other modules. I think the assignments were not too easy - to me the difficulty was just right. It's an introductory course to this matter and the worst you can do is daunt learners with unrealistic assignments (as in Week 4 of Text Mining). I think my appreciation for this course is intensified by the irritation with other courses. But at any rate, great job Daniel.

創建者 Matthew J

Jun 04, 2018

An excellent course which is well planned and executed! If you're following the specialization, it's a welcome relief after the text analysis course.

創建者 Rocco C

Oct 09, 2017

Very interesting course, thank you. The assignments could have been a bit more challenging.

創建者 Dea L

Dec 27, 2017

Excellent course!

創建者 datascience

Oct 24, 2017

Great course. Thank you.

創建者 Liran Y

May 20, 2018

Interesting and fun. Daniel's lecturing style is clear and enjoyable.

創建者 Kyle A

Sep 27, 2017

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.