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Вернуться к Applied Social Network Analysis in Python

Отзывы учащихся о курсе Applied Social Network Analysis in Python от партнера Мичиганский университет

4.7
звезд
Оценки: 2,550
Рецензии: 430

О курсе

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....

Лучшие рецензии

NK

2 мая 2019 г.

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

23 сент. 2018 г.

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.

Фильтр по:

301–325 из 423 отзывов о курсе Applied Social Network Analysis in Python

автор: Pranab d

17 мар. 2020 г.

awesome

автор: Light0617

1 июня 2019 г.

great!!

автор: Jeff D

13 янв. 2021 г.

Thanks

автор: Yoselin A

8 сент. 2020 г.

Great!

автор: Santosh R

31 мая 2020 г.

awsome

автор: ZHUOFU L

31 мар. 2019 г.

Great!

автор: SHREYASHI D

17 сент. 2020 г.

great

автор: Yash B

24 мая 2020 г.

great

автор: Muhammad M M

26 янв. 2020 г.

Good!

автор: Deleted A

5 дек. 2018 г.

great

автор: Gerardo M C

17 нояб. 2017 г.

Nice!

автор: Maxerom24

25 дек. 2021 г.

Best

автор: WANG Y

28 авг. 2021 г.

good

автор: Ankit K G

25 окт. 2020 г.

good

автор: Gudimetla v n r

20 сент. 2020 г.

nice

автор: Murugeswari P

13 авг. 2020 г.

good

автор: RAGHUVEER S D

25 июля 2020 г.

good

автор: Heshan L

17 июля 2020 г.

good

автор: SUTHAHAR P

2 июня 2020 г.

Good

автор: Hewawitharanage A H

31 янв. 2020 г.

good

автор: Parul S

20 апр. 2019 г.

good

автор: Akash G

3 мар. 2019 г.

good

автор: Deleted A

17 авг. 2018 г.

Wow

автор: M A

11 мая 2019 г.

ok

автор: David C

21 сент. 2017 г.

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.