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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

4.7
별점
2,573개의 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

NK

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.

JL

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 351~375

교육 기관: Eric M

2017년 10월 9일

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!

교육 기관: Raul M

2018년 7월 6일

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.

교육 기관: Vishal S

2018년 7월 16일

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.

교육 기관: Steffen H

2018년 11월 20일

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.

교육 기관: Edvard M

2022년 8월 1일

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

교육 기관: Sean D

2019년 6월 26일

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.

교육 기관: Ezequiel P

2020년 9월 16일

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

교육 기관: YUJI H

2018년 6월 28일

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.

교육 기관: Alejandro B

2020년 1월 10일

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

교육 기관: Martin U

2019년 1월 27일

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.

교육 기관: Tom M

2017년 11월 4일

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.

교육 기관: Grace B

2020년 4월 16일

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

교육 기관: Dmitry B

2017년 9월 14일

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

교육 기관: Victor G

2018년 10월 31일

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

교육 기관: Daniel D A

2020년 3월 28일

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

교육 기관: Lucas G

2017년 9월 21일

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

교육 기관: Yu C

2021년 11월 2일

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

교육 기관: Mike W

2019년 11월 20일

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

교육 기관: Shashi T

2018년 11월 17일

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

교육 기관: Bart C

2018년 12월 10일

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

교육 기관: Juan V P

2019년 8월 14일

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

교육 기관: Gregory C

2020년 4월 4일

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

교육 기관: Mohit M K

2018년 10월 22일

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

교육 기관: Anand K

2018년 11월 16일

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

교육 기관: Juan M

2019년 6월 11일

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