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Applied Social Network Analysis in Python に戻る

ミシガン大学(University of Michigan) による Applied Social Network Analysis in Python の受講者のレビューおよびフィードバック

4.7
2,572件の評価

コースについて

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....

人気のレビュー

NK

2019年5月2日

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

2018年9月23日

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.

フィルター:

Applied Social Network Analysis in Python: 351 - 375 / 427 レビュー

by Eric M

2017年10月9日

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!

by Raul M

2018年7月6日

Great class for an introduction to networks.I didn't give it 5 stars because it didn't give me enough information to apply the concepts learned to real life projects.

by Vishal S

2018年7月16日

Lectures are very well-designed. Especially, the assignment of week 4 is too good, that give me an overview of how we can apply machine learning in network analysis.

by Steffen H

2018年11月20日

Course was ok, the assignments are not too difficult. I wish the course would provided more insights and discussions of the presented metrics of centrality though.

by Edvard M

2022年8月1日

Would have appreciated more theoretic approach even for applied science course, but did like the content & much appreciate staff on being so helpful in forums

by Sean D

2019年6月26日

Overall, good course. It could use more explicit examples of NetworkX in the actual Jupyter Notebook itself, but the coverage of the material is high quality.

by Ezequiel P

2020年9月16日

Great course! The topic is very interesting! I would have liked it to have more hands-on approach during the lectures, but the course quality is great

by YUJI H

2018年6月28日

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.

by Alejandro B

2020年1月10日

Great course, however, there is quite complicated the autograder system. Sometimes it takes too much time trying to figure out technical issues.

by Martin U

2019年1月27日

This was a great course, lots of great insights to gain. Only thing that was frustrating was the multiple choice quiz questions. I hated those.

by Tom M

2017年11月4日

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.

by Grace B

2020年4月16日

The course provides a good overview of basic measures for network data. I took as prep for a harder course. I would recommend it.

by Dmitry B

2017年9月14日

This course was easier that the previous 4 in the specialization as it used them as a foundation for practical graph analysis.

by Victor G

2018年10月31日

Intreesting and rich in learning. The last assignment was specially fun. Would be nice with more such free assignments.

by Daniel D A

2020年3月28日

I liked the lectures but the assignments were significantly harder and had content that we didn't learn in the lecture

by Lucas G

2017年9月21日

Nice overview of general graph theory, and some useful exercises on how it can be applied for social network analysis.

by Yu C

2021年11月2日

This instructor in a lot better than the one in the text mining course, and the course content is better prepared.

by Mike W

2019年11月20日

If you've had prior expose to graphs (e.g., an intermediate-level CS course), the first 2.5 weeks is pretty easy.

by Shashi T

2018年11月17日

This was wonderful course in terms of content and content delivery. Prof was really nice. His pace was very good.

by Bart C

2018年12月10日

Great course! Love the instructor. Good background in networks, while sticking to the applied side of things.

by Juan V P

2019年8月14日

Good course with a nice and clean talk professor. Perhaps I miss some real-world cases in the assignments.

by Gregory C

2020年4月4日

Pretty well designed course, except that I found myself battling the auto-grader too often.

by Mohit M K

2018年10月22日

One of the more tougher courses in Social Networks but still would recommend to everyone!

by Anand K

2018年11月16日

Good Content! And the assignments were just right to augment effective learning.

by Juan M

2019年6月11日

The machine learning connection could have been mentioned earlier in the course