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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

1,422 個評分
229 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.


201 - Applied Social Network Analysis in Python 的 222 個評論(共 222 個)

創建者 Raul M

Jul 06, 2018

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.

創建者 Vinicius G

Jan 29, 2018

The explanations were very really good and clear but not enough to complete the assignments. The assignments were over the top in difficulty. The hardest in the entire course program. That is the only reason I took one star. It was because I felt that the classes did not prepare for the assignments. Or, assignments should have a more clear explanation of the steps to be taken in order to complete them. Definitely we should look for answers ourselves but not being able to clearly understand each step throughout the assignments really limited my research area and increased my frustration.

創建者 Andreas C

Dec 03, 2017

quite good

創建者 Selvakumar

Jun 20, 2018

This is awesome course!

創建者 Harshith S

Jun 24, 2019

Daniel Romero is probably the best instructor in this specialization

創建者 Sean D

Jun 26, 2019

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.

創建者 Juan M

Jun 11, 2019

The machine learning connection could have been mentioned earlier in the course


Aug 02, 2019

Good course

創建者 Avi R

Aug 03, 2019


創建者 Juan V P

Aug 14, 2019

Good course with a nice and clean talk professor. Perhaps I miss some real-world cases in the assignments.

創建者 John W

Jun 11, 2019

This was a good course. I learned a good amount about network analysis and the python library networkx. I can envision using what I learned in my job. However, of the five courses in the Applied Data Science with Python Specialization I felt this was the weakest offering.

1. The Title. While the majority of the examples and exercises were focused on social networks, there's little in the course that is really specific to social networks. The course applies to any kind of network that can be loaded into networkx.

2. Trim the Process Descriptions. Too often the lecturer would say things like "Node A has degree of 3 because it is connected to three other nodes. Node B has a degree of 5 because it is connected to five other nodes. Node C has a degree of 4 because it is connected to four other nodes." For such a simple concept, that many examples aren't needed.

3. Provide On-Screen Example Files (my biggest gripe). In all of the previous courses, when the lecturer gave code examples on screen, there was a corresponding Jupyter notebook with those examples so the learner could follow along, and keep the notebook as a handy refresher of how to interact with the library. None of that was provided in this course.

創建者 Chethan S L

Oct 02, 2019


創建者 Leon V

Oct 08, 2017

it was okay, 3.5 really

創建者 Siddharth S

Jun 14, 2018

The Course Deserves 5 Stars BUTThe fundamental flaw that felt absent in the last two courses of the specialisation was the in lecture Jupyter Notebook Demonstrations, it really helped the students feel in sync with the mentors.Please correct the same all the 5 courses of this specialisation deserve 5 starts :)

創建者 Mark H

Feb 07, 2018

I liked the lecturer and the tempo of the lectures, but this course felt a little light compared to the others in the specialization. The quizes were also good. But for me the course was a bit off topic. Given that, the various skills I learned in the other courses did come together in the final programming assignment. As a stand alone course I would give it four stars, but it gets three because it's required for the data science specialization.

創建者 József V

May 05, 2018

Useful but weaker comparing to Pandas or Scikit courses.


Apr 07, 2018


創建者 Sara C

May 17, 2018

i like the way that lecturer teach.

創建者 XU D

Oct 13, 2017

The assignment auto grader was horribly designed.

創建者 Ryan D

Aug 10, 2019

The specialization for Applied Data science started strong, with engaging exercises, good instruction, and good recommendations for additional reading and resources. As the specialization continued, the courses seemed to get "lazy", and the course topics became more abstract and less applied.

After going through this specialization, I would not recommend this to someone if I could find a better program through edX or another coursera offering.

創建者 Kevin c

Aug 14, 2019

For a coding heavy course, why doesn't the instructor just upload the code used in slides as a Jupyter Notebook? This would save A LOT OF TIME and frustration. Right now, I have to pause the video to copy the code AND write my own notes and it wastes so much time. Not to mention, you can easily be prone to writing wrong syntax when you're trying to keep up so fast, and then you run the code chunk and it doesn't work and you have to go back to that point in the video. It's a simple staple that I would have expected in a UMich course. Also, they don't show how to create networks from pre-existing data, which is how you will usually work in the real-world

創建者 Eric S

Oct 28, 2018

They need to change the 4th assignment is almost impossible to run on jupyter