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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: 2,492
Рецензии: 420

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:

376–400 из 410 отзывов о курсе Applied Social Network Analysis in Python

автор: Subeesh P

9 нояб. 2020 г.

Very good experience

автор: Andrés M A

7 мар. 2021 г.

Excellent course.

автор: Nicolas B

6 окт. 2017 г.

Very good Course.


16 окт. 2020 г.

nicely explained

автор: arpit m

15 дек. 2018 г.

very good course

автор: Raghunath P

10 нояб. 2018 г.

Great Course!

автор: Vinit D

16 янв. 2020 г.

Tough course

автор: Avi R

3 авг. 2019 г.


автор: Jean E K

18 мая 2018 г.

good teacher

автор: TEJASWI S

2 авг. 2019 г.

Good course

автор: Andreas C

2 дек. 2017 г.

quite good

автор: Chethan S L

2 окт. 2019 г.


автор: Xing W

3 дек. 2017 г.

Not bad

автор: shubham z

13 июня 2020 г.


автор: Mallikarjuna R Y

5 мая 2020 г.


автор: V B

30 дек. 2020 г.


автор: Mark H

7 февр. 2018 г.

I liked the lecturer and the tempo of the lectures, but this course felt a little light compared to the others in the specialization. The quizes were also good. But for me the course was a bit off topic. Given that, the various skills I learned in the other courses did come together in the final programming assignment. As a stand alone course I would give it four stars, but it gets three because it's required for the data science specialization.

автор: Siddharth S

14 июня 2018 г.

The Course Deserves 5 Stars BUTThe fundamental flaw that felt absent in the last two courses of the specialisation was the in lecture Jupyter Notebook Demonstrations, it really helped the students feel in sync with the mentors.Please correct the same all the 5 courses of this specialisation deserve 5 starts :)

автор: Alexandra C

28 февр. 2021 г.

Videos are very distracting as there are many cutscene from the text to the instructor's face which is very disrupting for the flow of the lecture. Maybe overlaying his face on a small window on the corner will be better

автор: Daniel B

18 дек. 2020 г.

This course feels more like an API summary of networkx rather than a real course on social network analysis. On top of that, the course uses the outdated networkx 1.11, while 2.0 has been out for over three years.

автор: Jeremy .

1 янв. 2021 г.

Some of the assignment organization could have been better, but otherwise the information was rock solid!

автор: Jenny z

1 дек. 2020 г.

better if TA could prepare projects with updated versions of libraries

автор: József V

4 мая 2018 г.

Useful but weaker comparing to Pandas or Scikit courses.

автор: Sara C

16 мая 2018 г.

i like the way that lecturer teach.

автор: Leon V

8 окт. 2017 г.

it was okay, 3.5 really