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Avis et commentaires pour l'étudiant pour Applied Social Network Analysis in Python par Université du Michigan

4.6
1,473 notes
238 avis

À propos du cours

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....

Meilleurs avis

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.

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1 - 25 sur 230 Examens pour Applied Social Network Analysis in Python

par 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.

par 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.

par 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.

par 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.

par Wei W

Dec 09, 2018

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

par Daniel W

Feb 19, 2019

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

par Ahmad H S

Aug 05, 2019

it is good but we are looking for more real practices

par 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.

par 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

par XU D

Oct 13, 2017

The assignment auto grader was horribly designed.

par Eric S

Oct 28, 2018

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

par SONIA D

Jan 27, 2019

Very useful

par Alexander G

Feb 05, 2019

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.

par charles l

Feb 04, 2019

A completely new area for me, and a really fascinating course.

par Nikolay S

Jan 02, 2019

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.

par Michael

Dec 17, 2018

Great job!

par PREMAL M

Feb 24, 2019

Excellent delivery and content.

par CMC

Feb 14, 2019

This is a great course for 2 reasons. The earlier assignments were just difficulty enough to reinforce the lectures. The last assignment was challenging enough to bring the entire specialization to to satisfying close. After finishing assignment 4, I really feel that I can apply the learning from this specialization to real work.

par Kristin A

Feb 15, 2019

A nice intro to networks in Python

par Aya

Feb 26, 2019

The course covered many relevant topics and was very easy to follow and apply to the real world.

par Akash G

Mar 03, 2019

good

par Varga I K

Mar 09, 2019

It was great introducing the networks, but I found most of the assignments too straightforward except for the last weeks.

par ABDUL N M

Mar 12, 2019

Gave me a very good understanding of the basic concepts

par Yunhong H

Mar 23, 2019

Great course. The lectures are taught clearly. The knowledge gained in this course is very useful in real world.

par Roberto L L

Mar 26, 2019

It was a wonderful course, linked network's models and machine learning.