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미시건 대학교의 Applied Social Network Analysis in Python 학습자 리뷰 및 피드백

4.7
1,207개의 평가
207개의 리뷰

강좌 소개

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....

최상위 리뷰

NK

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.

JL

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.

필터링 기준:

Applied Social Network Analysis in Python의 201개 리뷰 중 1~25

교육 기관: Oliverio J S J

Feb 25, 2018

This course is a good introduction to graph theory. Its contents are interesting and the lecturer did a great job explaining them. So, what is the problem? The problem is that the course is not called "Applied Graph Analysis in Python" but "Applied Social Network Analysis in Python". This incongruity in the title of the course (intentional or not) will generate erroneous expectations in the students, especially if we consider that they have to take the course to finish the specialization. Regarding the assignments, they are divided into two groups: trivial tasks that are solved with a single line of code extracted from the NetworkX manual and more complex tasks related to Machine Learning that do not involve putting into practice the concepts of this course but those of the third course of the specialization. I regret being so tough, but my impression on this course is that it is filler content designed just to have a five course specialization instead of four.

교육 기관: Aziz J

Dec 28, 2017

Going into this course, I was really disappointed that I had to take this course for a Data Science Specialization because at a skin-deep level it seemed very irrelevant, and frankly I was at that state of mind until week 4 of this course.

There are several reasons why I'm rating this course 2 out of 5 stars:

1) The content of the first three weeks were just informational and should have been covered in one or two weeks.

2) Homework assignments were not challenging at all. 90% of the questions were one-liners and required simply calling the methods of networkx that was discussed. This course would benefit by homework assignments that had 1-2 problems that required us to solve real-life problems from scratch, rather than ONLY calling networkx methods.

3) There was no discussion on how to get network data. We were just given all this magical data about how relationship scores between employees and future connections between employees... How am I supposed to get that in real life?? Some problems asking us to make a network would've been valuable.

4) More time should have been spent on prediction and other advanced topics, at least another week to bring the "Applied" into "Applied Social Network Analysis."

5) I really enjoyed the professor's teaching style. He explained concepts well and had great examples during lectures.

교육 기관: Daniel W

Feb 19, 2019

Great course, maybe even the best on this great specialization!

교육 기관: Wei W

Dec 09, 2018

This is by far my favorite Coursera course - well organized contents and intuitive example!

교육 기관: David M

Nov 15, 2018

This is hands down the best taught course in the speciality. The instructor explains concepts in the videos clearly and the assignment questions are structured and interesting. Do note that the assignment in week 4 does pull together the whole specialisation in a real world problem, so if you aren't taking the whole speciality you will need a knowledge of Pandas and SKLearn. Personally I thought it was pitched at just the right level because the ML work is just enough to have to go through the process, without any complicated feature optimisation.

Only wish the other courses worked as well as this one.

교육 기관: Luis d l O

Mar 02, 2018

The lectures are good. However, the assignments are poor: very simple exercises with toy examples, but far away from real applications. Moreover, I spent most of the time (particularly in the last assignment) trying to deal with the autograder.

교육 기관: jose H C

Jul 14, 2019

Interesting.!

교육 기관: Ruihua G

Jul 08, 2019

this course provided a overview of the network analysis.

교육 기관: 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.

교육 기관: Harshith S

Jun 24, 2019

Daniel Romero is probably the best instructor in this specialization

교육 기관: Shadi A

Jun 23, 2019

Great course

교육 기관: Daniel R

Jun 20, 2019

My favourite course

교육 기관: John H E O

Jun 16, 2019

Amazing course!!!

교육 기관: Anurag B

Jun 12, 2019

Great Content!!

교육 기관: Dhananjai S

Jun 11, 2019

One of the best courses on social network analysis. Professor Daniel Romero did an excellent job explaining the contents.

교육 기관: 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.

교육 기관: Juan M

Jun 11, 2019

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

교육 기관: Slavisa D

Jun 09, 2019

Very helpful, I didn't know anything about graphs, networks modelling and the NetworkX package before this course.

교육 기관: Sarah H H

Jun 03, 2019

i found this course to be fun and straightforward. The assignments were directly aligned to instruction. Great course!

교육 기관: Ivan S F

Jun 02, 2019

Very very good course. It provides a brief but comprehensive introduction to network analysis.

교육 기관: Nussaibah B R S

Jun 02, 2019

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.

교육 기관: Light0617

Jun 01, 2019

great!!

교육 기관: Carl W

May 30, 2019

Month 5 was very nice. I enjoy networks and appreciate your presentation of the material. I would also like to thank all of those who worked to bring the specialization to life. This includes the lecturers, grad students, and mentors who devoted time to the class.

THANKS!!

교육 기관: Ho C

May 30, 2019

Great course with clear instructions

교육 기관: Henri

May 19, 2019

Great intro to networks; last assignment is challenging but is a good opportunity to put everything together (python+ML+Network).