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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

Оценки: 2,490
Рецензии: 419

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

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.

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:

351–375 из 410 отзывов о курсе Applied Social Network Analysis in Python

автор: Gregory C

4 апр. 2020 г.

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

автор: Mohit M K

22 окт. 2018 г.

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

автор: Anad K

16 нояб. 2018 г.

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

автор: Juan M

11 июня 2019 г.

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

автор: Minshen C

25 дек. 2019 г.

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

автор: Jonas N

5 окт. 2018 г.

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

автор: Divyansh R

12 мая 2020 г.

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

автор: Miguel C

8 дек. 2017 г.

The last assigment is really interesting, all the others are really easy

автор: Maciej W

7 сент. 2018 г.

Great hands on learning experience to social network analysis in Python

автор: Deepalakshmi K

24 июня 2019 г.

Daniel Romero is probably the best instructor in this specialization

автор: Lorenzo V ( R P

22 мая 2021 г.

Great class but, please, fix the autograder, guys. No, really do.

автор: Roger v S

6 окт. 2020 г.

The lecturer was the best of the lecturers in the specialisation.

автор: Carlos F S B

19 авг. 2020 г.

it was really difficult for me, gotta practice more on my own

автор: Jesús P

22 янв. 2018 г.

Good course but could be improved with realistic scenarios.

автор: Siwei Y

21 сент. 2017 г.

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。

автор: Yang F

22 сент. 2017 г.

The first three weeks are very well planned.

автор: Christian E

27 мар. 2019 г.

Very new on this topic and very interesting

автор: David W

12 окт. 2017 г.

Challenging course and great instruction.

автор: Brian R v K

23 окт. 2017 г.

Great fun, with practical application.

автор: Robert S

29 нояб. 2020 г.

Interesting material well presented.

автор: Oscar F R P

30 авг. 2020 г.

Really hard but very interesting.

автор: Cyrus N A P

24 янв. 2019 г.

Well the subject was really hard.

автор: Deni M

30 мая 2021 г.

G​reat course highly recommended

автор: Rupert

23 янв. 2018 г.

Good introduction into graphs!

автор: Selvakumar

20 июня 2018 г.

This is awesome course!