Chevron Left
Вернуться к Applied Social Network Analysis in Python

Отзывы учащихся о курсе Applied Social Network Analysis in Python от партнера Мичиганский университет

4.7
звезд
Оценки: 2,492
Рецензии: 420

О курсе

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....

Лучшие рецензии

NK
2 мая 2019 г.

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
23 сент. 2018 г.

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.

Фильтр по:

26–50 из 410 отзывов о курсе Applied Social Network Analysis in Python

автор: Jiunjiun M

14 апр. 2018 г.

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.

автор: John K

16 сент. 2021 г.

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 And the course uses version 1.11 of NetworkX which is woefully outdated.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.

автор: Ajit P

10 мая 2020 г.

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.

автор: Frank L

14 окт. 2017 г.

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!

автор: Nikolaos K

10 февр. 2021 г.

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.

автор: Rahul S

7 окт. 2018 г.

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.

автор: Rishabh M

20 июля 2020 г.

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.

автор: Subramanian A

3 янв. 2021 г.

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 !!

автор: Abu S

10 мая 2020 г.

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.

автор: Yusuf E

24 сент. 2018 г.

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.

автор: Jonathan B

14 июля 2020 г.

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.

автор: CMC

14 февр. 2019 г.

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.

автор: Keary P

21 апр. 2019 г.

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.

автор: Ricardo S

27 окт. 2020 г.

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.

автор: Víctor L

23 мар. 2018 г.

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.

автор: Niranjan H

13 нояб. 2018 г.

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.

автор: Santiago D D

22 апр. 2019 г.

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.

автор: Carl W

30 мая 2019 г.

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!!

автор: 王玉龙

18 окт. 2017 г.

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...

автор: Praveen R

10 дек. 2019 г.

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.

Praveen

автор: Dongliang Z

18 янв. 2018 г.

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!)

автор: Dung D L

14 сент. 2020 г.

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.

автор: john w

21 апр. 2018 г.

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.

автор: Nikolay S

2 янв. 2019 г.

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.

автор: sampath A B

2 дек. 2020 г.

I have really enjoyed the course ("Applied Social Network Analysis in Python."I like the way you summarize each module at the end of the module. I think others should learn from you.However, the python "Networkx" library is very annoying.