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Avis et commentaires pour d'étudiants pour Applied Social Network Analysis in Python par Université du Michigan

4.7
étoiles
2,529 évaluations
425 avis

À propos du cours

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....

Meilleurs avis

NK
2 mai 2019

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 sept. 2018

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.

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401 - 416 sur 416 Avis pour Applied Social Network Analysis in Python

par Daniel B

18 déc. 2020

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 On top of that, the course uses the outdated networkx 1.11, while 2.0 has been out for over three years.

par Jeremy .

1 janv. 2021

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

par Jenny z

1 déc. 2020

better if TA could prepare projects with updated versions of libraries

par József V

4 mai 2018

Useful but weaker comparing to Pandas or Scikit courses.

par Sara C

16 mai 2018

i like the way that lecturer teach.

par Leon V

8 oct. 2017

it was okay, 3.5 really

par DAWUN J

6 avr. 2018

hm..

par Afreen F

7 févr. 2021

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.

par MENAGE

22 févr. 2021

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

par Natasha D

5 déc. 2019

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.

par Hiroki T

26 mars 2021

Python and related libs are SUPER old. Some important codes used in this specialization were duplicated and you cannot get enough explanations even on Google. Moreover, auto-graders have lots of problems. I finished this specialization but I cannot recommend this.

par Moustafa S

19 août 2020

not usefull course, out dated materials and it doesn't work on new library, what's the use of it if it doesn't work anymore and noone uses it?

par A P

5 juil. 2021

All assignments and lectures are outdated and will not work with current versions of Python.

par Christopher S

8 mai 2021

Vague, little explanation, I can get a better education on Udemy

par Sonam A

18 déc. 2019

not interesting.

par José T

27 juil. 2021

N​ot updated.