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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: 2,441
Рецензии: 409

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:

176–200 из 398 отзывов о курсе Applied Social Network Analysis in Python

автор: Sanjay K

22 янв. 2018 г.

Michigan course everything is excellent. love it

автор: Eagan C

1 дек. 2020 г.

Very clear in explaining these useful concepts

автор: Eric W

3 февр. 2020 г.

Clear, concise, well organised and structured.

автор: Sebastian B B

24 авг. 2020 г.

Manual calculation for quizes are useless imo

автор: Xin Y

3 апр. 2020 г.

Excellent Course! Best in the specialization!

автор: Konstantinos M

13 сент. 2018 г.

Very interesting topic and well-made lessons.

автор: Ho P D

26 мая 2021 г.

Nice course with broad overview of the topic

автор: phantomxx

8 окт. 2020 г.

Great content and practices! Really useful.

автор: Mohammad H

26 окт. 2018 г.

the course will teach basic of SNA so clear

автор: Ayush R

5 авг. 2018 г.

Better Explanation, Not too hard to solve .

автор: Fengping W

1 мая 2018 г.

It is really a good series, I learned a lot

автор: Sagar

29 дек. 2018 г.

Grate for solving network analytics issues

автор: John A C

18 нояб. 2019 г.

I loved learning all about graph theory!

автор: hssni A

3 нояб. 2020 г.

very well explained thank you very much

автор: Luiz H

25 сент. 2017 г.

Great course, very informative. Thanks!

автор: Bruce M

27 апр. 2020 г.

Great course, Interesting assignments.

автор: Behzad M

19 янв. 2020 г.

Very interesting, I have learnt a lot.

автор: Dongquan S

22 окт. 2019 г.

Very well organized course. Thank you!

автор: Tina L

16 дек. 2017 г.

Good Elaboration. Very clear concepts.

автор: Esteban L C

18 дек. 2020 г.

Fast shipping. Item as described. A++

автор: Ivan

7 июля 2020 г.

Was an Intresting and awesome course

автор: chenshenyou

13 апр. 2020 г.

very nice graph training, good work!

автор: Ho C

30 мая 2019 г.

Great course with clear instructions

автор: Sangeeth S

5 сент. 2020 г.

Very interesting course! Loved it!

автор: Kristin A

15 февр. 2019 г.

A nice intro to networks in Python