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.
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.
創建者 Anad K•
Nov 16, 2018
Good Content! And the assignments were just right to augment effective learning.
創建者 Shashi P T•
Nov 17, 2018
This was wonderful course in terms of content and content delivery. Prof was really nice. His pace was very good.
創建者 Maciej W•
Sep 07, 2018
Great hands on learning experience to social network analysis in Python
創建者 Bernardo A•
Oct 08, 2017
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.
創建者 Chris M•
Oct 08, 2017
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.
創建者 Yang F•
Sep 22, 2017
The first three weeks are very well planned.
創建者 Miguel C•
Dec 08, 2017
The last assigment is really interesting, all the others are really easy
Jan 23, 2018
Good introduction into graphs!
創建者 David C•
Sep 21, 2017
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.
Feb 25, 2018
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?
創建者 Tom M•
Nov 05, 2017
A bit confusing material since it is new to me. Lots of material in a short course. The auto grader is a bit difficult to work with.
創建者 Brandan S•
Sep 19, 2017
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.
創建者 Chad A•
Jan 13, 2018
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.
創建者 Brian R v K•
Oct 23, 2017
Great fun, with practical application.
創建者 Srinivas K R•
Oct 09, 2017
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.
Sep 19, 2017
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.
創建者 Xing W•
Dec 04, 2017
創建者 Jesús P S•
Jan 22, 2018
Good course but could be improved with realistic scenarios.
創建者 Dmitry B•
Sep 14, 2017
This course was easier that the previous 4 in the specialization as it used them as a foundation for practical graph analysis.
創建者 Nicolas B•
Oct 06, 2017
Very good Course.
創建者 Lucas G•
Sep 21, 2017
Nice overview of general graph theory, and some useful exercises on how it can be applied for social network analysis.
創建者 Yuzi H•
Jun 28, 2018
The presentation documents are very helpful to understand the lectures. If they can be downloaded to our local laptop, I evaluate this course 5 stars.
創建者 Eric M•
Oct 09, 2017
This was an excellent overview of using and analyzing graphs with Python. I learned a lot, got to apply my learning from previous courses, and I earned my Specialization!
創建者 Siwei Y•
Sep 21, 2017
老师讲解的非常好 ， 逻辑清楚，条理明晰。建议编程作业稍微有点难度。所以扣掉一颗星。 希望越来越好。
創建者 David W•
Oct 12, 2017
Challenging course and great instruction.