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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: 2,569

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:

401–425 из 427 отзывов о курсе Applied Social Network Analysis in Python

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: 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

автор: DW J

6 апр. 2018 г.


автор: Afreen F

7 февр. 2021 г.

Lecture Videos are good but it seems 0 efforts were put in the assessments. The auto-grader is especially a pain and you end up spending LOT of time around trivial issues with the auto-grader.

автор: MENAGE

22 февр. 2021 г.

Aimerais avoir plus de temps et de conseils pour bien réussir..

автор: Natasha D

5 дек. 2019 г.

The lectures and first three assignment are extremely superficial. Mostly they throw a bunch of definitions of metrics at you, give you some one-liners that will calculate specific metrics, then ask you to spit back those one liners (essentially no discussion of applications, etc). Then the fourth and final assignment is an interesting application of what you've learned but the grader is a NIGHTMARE. It is super buggy and your true task is to learn how the grader works, not how to write code and apply what you've learned about data science. I would not recommend this course unless you need it to finish the specialization.