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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

4.6
별점
1,761개의 평가
290개의 리뷰

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

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.

JL

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.

필터링 기준:

Applied Social Network Analysis in Python의 283개 리뷰 중 226~250

교육 기관: 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!

교육 기관: Raul M

Jul 06, 2018

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

Jul 16, 2018

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

Nov 21, 2018

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: Sean D

Jun 26, 2019

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: YUJI 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.

교육 기관: Alejandro B

Jan 11, 2020

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

Jan 28, 2019

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

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.

교육 기관: cadmium

Apr 16, 2020

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

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.

교육 기관: bictor

Oct 31, 2018

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

교육 기관: Daniel B d A A

Mar 29, 2020

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

Sep 21, 2017

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

교육 기관: Mike W

Nov 21, 2019

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 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.

교육 기관: Bart T C

Dec 10, 2018

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

교육 기관: Juan V P

Aug 14, 2019

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

교육 기관: Gregory C

Apr 04, 2020

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

교육 기관: Mohit M K

Oct 22, 2018

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

교육 기관: Anad K

Nov 16, 2018

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

교육 기관: Juan M

Jun 11, 2019

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

교육 기관: Minshen C

Dec 25, 2019

it would be great if some case study of prediction can be added to the course

교육 기관: Jonas N

Oct 05, 2018

Highly valuable course and a good starter for network analysis. Do recommend!

교육 기관: Divyansh R

May 12, 2020

Great instructor. Very engaging videos and thought-provoking assignments.