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Applied Social Network Analysis in Python(으)로 돌아가기

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

2,571개의 평가

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


2019년 5월 2일

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.


2018년 9월 23일

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 427개 리뷰 중 326~350


2020년 6월 2일


교육 기관: Hewawitharanage A H

2020년 1월 31일


교육 기관: Parul S

2019년 4월 20일


교육 기관: Akash G

2019년 3월 3일


교육 기관: Deleted A

2018년 8월 17일


교육 기관: M A

2019년 5월 11일


교육 기관: David C

2017년 9월 21일

This was, in general, a good course. The instructor was very clear in what he presented, and gave a good overview of Social Network Analysis. However, there were several issues with the AutoGrader that did not get fixed until late in the course and the PowerPoint slides for the lectures were also very late in getting posted (they were not available for most of the programming assignments). So, I think this course was launched a little early. Still, these are problems that you might expect to see the first time a course is taught and should not affect future students.

The bigger complaint I have on the course was that it was a very gentle introduction of the topic with only a quick overview of the subject. The lectures themselves concentrated more on a litany of various measures and metrics to characterize networks and could have benefited from a broader examination of real networks in the real world. One of the most interesting topics was a very quick overview of plotting for network diagrams, but this was never followed up with a programming assignment or other aspects to give us practice using the techniques described. This course would benefit from 2-4 additional weeks of material and more programming assignments, IMO. The network graphing lecture, for example, could have been reinforced with a peer-graded assignment to have us produce 3 or 4 types of graphs of various networks.

Overall, though, I was pleased with this course and the entire specialization. I would definitely recommend it to others.

교육 기관: John W

2019년 6월 11일

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: Rui B

2018년 2월 25일

Extremely good introduction to network analysis. The course heavily relies on NetworkX, and doesn't require extensive programming knowledge - with the help of Google, you may easily solve all problems. The lectures were well structured and easy to follow. Having said this, I have found 2 major drawbacks: 1. I would really appreciate some external references so that I could get a theoretical introduction to the materials taught. 2. The last assignment required machine learning, which was not taught in this course. With the help of the forums and a bit of googling, it is easy to get full mark, but perhaps the authors could include such background in the provided notebooks?

교육 기관: Vinicius G

2018년 1월 29일

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.

교육 기관: Aino J

2020년 7월 2일

I started the course only because it was part of the Specialisation, but I am glad I did because the topic is actually very interesting! This course covers the basics. The lectures are very well structured, quizzes are suitably challenging, and the assignments are interesting while not terribly challenging. You'll apply some of the machine learning concepts from course 3 in the final week's assignments, which I though was a nice, round finish to the Specialisation.

교육 기관: VenusW

2017년 9월 19일

Learnt considerable amount about social network from this course, as introductory level, materials (lectures and assignments) are well-prepared, much better than course 4 (text-mining). Assignments are not too hard, probably has relative good foundation from previous 4 courses. Auto-grader is a real pain in this specialization (course 3, 4 and 5), need to go through thorough test before release.

Do not consider this specialization as intermediate level.

교육 기관: Vani K - P

2020년 6월 12일

Its a amazing course for beginners with little Python experience. The lectures and quiz are simple and assignments are really challenging. If you are looking for Social Networks course which covers nook and corners of Social Networks Analysis then this course is not for you.

교육 기관: Brandan S

2017년 9월 19일

Pro: Required interpretation of methods presented for application on assignments without explicit direction. Required application of knowledge gained in previous specialization courses.

Con: Explanations of social network analyses were limited in number and shallow in coverage.

교육 기관: Robert J K

2018년 12월 18일

The course starts off a bit slow but gets you used to the NetworkX module. The last exercise is a pretty neat culmination of the this course and specialization. It would have been cool for it to also involve text mining, but I enjoyed it and the course in general.

교육 기관: Carlos F P

2020년 2월 7일

The course provides a great introduction to graph analytics, I consider that the social network applications are very sparse or missing in action altogether. Nonetheless, overall great content and practice of extracting information from networks with Python.

교육 기관: Jose P

2018년 12월 8일

Social Network was completely new to me and I found this course provided basic and more detailed information about the matter, and also enough documentation to continue learning. I see there is much more to learn, but the course was a great introduction.

교육 기관: Thomas L

2021년 1월 26일

Course was very straightforward application of the lecture materials. Not as challenging as the first three courses of this specialization, but nevertheless it was instructed very clearly and was informative. Would recommend this course.

교육 기관: Srinivas R

2017년 10월 9일

Good overview of network concepts using networkx - wish the course were a few weeks longer for it finishes just when you feel you can begin to something useful with the basics you have learned - but you do learn the basics.

교육 기관: bob n

2020년 9월 22일

Good basic course, well paced. I liked the instructor. Weekly assignments fair, some tougher than others. Occasionally finicky Auto grader a bit like artillery, need to send a couple of rounds over to home in on target.

교육 기관: Devansh K

2020년 12월 28일

Extremely detailed and challenging course. The assignments require a lot of thinking and skill. Gives a comprehensive overview of social network analysis and a good way for any novice python coder to improve their skills

교육 기관: Bernardo A

2017년 10월 8일

Really good overview of concepts and analysis related to 'graphs'. Could be more challenging when it comes to projects: for example, teach students to gather real data from twitter or facebook and make graphs with it.

교육 기관: Chris M

2017년 10월 7일

I know its hard to go in deep detail with these courses. If you used one graph and gradually built upon it through the course it may reinforce the concepts better. Thoroughly enjoyed though, learned a lot.

교육 기관: Chad A

2018년 1월 13일

The material and assignments were great and well aligned. The autograder for the Jupyter Notebooks was finicky at best and resulted in lots of time wasted getting formatting correct.

교육 기관: Vivien A

2021년 3월 16일

Great content but assignment / auto grader sometimes difficult to deal with. In particular, errors not clearly described. Much time wasted due to wrong package version, etc. etc.