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Avis et commentaires pour d'étudiants pour Applied Social Network Analysis in Python par Université du Michigan

2,550 évaluations
430 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


2 mai 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.


23 sept. 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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26 - 50 sur 423 Avis pour Applied Social Network Analysis in Python

par Dishi T

9 août 2020

I have really enjoyed learning this course. All the concepts are explained with proper examples. This course not only provides theoretical knowledge about network analysis but also explains the use of each topic in real networks. The assignments were really helpful to get hands on experience of all the topics covered. The most interesting part of this course was the last assignment It was fun experimenting with different models and analyzing the performance.

par James M

30 mai 2018

This is the last course of the Applied Data Sci in Python certificate. It effectively ties together all the introduced concepts from the previous courses (except Natural Language Processing). Daniel Romero was an extremely effective lecturer and many of the concepts and know-how were introduced, taught, and assessed appropriately. I'm also impressed that I was able to learn a new python library I (or my coworkers) had not heard of before.

par Jiunjiun M

14 avr. 2018

I learned many interesting new concepts in social network analysis and a bunch of new graph algorithms, which are rarely taught in the "traditional" algorithm course. Now I know how companies like Cambridge Analytics can use the Facebook's social network data to derive useful information. (It's actually quite easy.) A class like this is more important than ever. I just wish we could have more time to explore a few topics more deeply.

par John K

16 sept. 2021

This course is a great way to learn about networks, how to build network models, and techniques to analyze them. The focus on applying fundamental concepts was useful, especially how network models can feed machine learning models. However, the course didn't cover accessing and analyzing data from popular social networks at all. And the course uses version 1.11 of NetworkX which is woefully outdated. A course update is badly needed.

par Ajit P

10 mai 2020

Everything in this course was new to me. I was always curious about social media products and how companies like Twitter and Facebook come with certain features in their offerings. This course is very introductory but it provides a good platform to develop interest and pursue more knowledge in social network analysis. I highly recommend this course to learn to decode social network analysis.

par Frank L

14 oct. 2017

This course was very interesting and well taught, finally after all other courses I have managed to complete the assignments for this one in the recommended amount of time. Maybe the questions were structured better than past modules, or maybe my level of understanding of programming in python was at its best. Either way the assignments were very enjoyable, thank you!

par Nikolaos K

10 févr. 2021

Very good course, networks can be used in almost every aspect of a business or market. We learned many ways to represent networks in python, and visualize them. The lecturer was very direct and to the point with his slides and examples; the summaries after each lesson are so useful. I would like the final assignment to be a lilttle more challenging, though.

par Rahul S

7 oct. 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.

par Rishabh M

20 juil. 2020

Excellent Course and Specialization. I learned a lot of techniques and tools through this specialization. The specialization has provided a new dimension to my knowledge and learning. Assignments were amazing. The cherry on top of the cake was last assignment of the last course, in which we used the knowledge from the first course to the last course.

par Subramanian A

3 janv. 2021

Excellent course with a broad overview of the networks an how python packages can be used for network analysis. There was a nice mix of conceptual sessions along with the usage of networkX for coding assignments. Thanks to UMich for putting this course together !! I put some of the concepts to work right from the day I learnt them. Awesome !!

par Abu S

10 mai 2020

I started this course with certain amount of nervousness since I did not have a lot of idea about network analysis. With time I really become interested in this subject and by the week 4 I was really fell in love with this subject. The teacher was very engaging and clearly explained the ideas. Looking forward to finishing the specialization.

par Yusuf E

24 sept. 2018

Coming into this course, I didn't expect much but I was pleasantly surprised by the quality of the material. The quizzes were especially designed well and the final assignment was really challenging and instructive. I wish there was more of predictive modeling using network features but the rest of the course easily makes up for that.

par Jonathan B

14 juil. 2020

I only took this course so that I could finish off the data science specialization and I was pleasantly surprised by how much I enjoyed it. Instructor did a great job of tying the content to real-world applications and I personally enjoyed the final project which utilized much of the material that was learned throughout the course.

par CMC

14 févr. 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 Keary P

21 avr. 2019

Nice way to end the 5 course specialization. Brought together several machine learning and python skills that I learned in the previous courses. Instructor does a great job introducing new concepts with high level theory and intuitive examples. Course slides were superb and can serve as future reference material.

par Ricardo S

27 oct. 2020

Great course. Clear content, both on theory & practical applications giving a good overview of Graphs/Networks analysis as well as Simulation. I enjoyed the programming exercises and in particular appreciated the possibility of using ML algorithms for prediction within a Network framework.

par Víctor L

23 mars 2018

Excellent Course, very interesting, no idea that so many tools existed for network study and analysis. Excellent job both from the professor Daniel, and from Coursera/University of Michigan State. The QUIZES were very challenging, sometimes more than the Assignments. I'm really satisfied.

par Niranjan H

13 nov. 2018

As a course by itself or as part of the specialization, either way (it helps to have completed the first two in the set), it is a great course.

It provides a very good high level picture of what is needed in ones toolbox.

Essentials: networkx, matplotlib and to a lesser extent pandas.

par Santiago D D

22 avr. 2019

This class was an excellent introduction to network analysis, where concepts, metrics and purpose of application where provided in a clear and digestible manners. The instructor made the class very livable with topics that might have been too dry under different circumstances.

par Carl W

30 mai 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.


par 王玉龙

18 oct. 2017

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...

par Praveen R

10 déc. 2019

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.


par Dongliang Z

18 janv. 2018

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!)

par Dung D L

14 sept. 2020

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.

par john w

21 avr. 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.