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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: 2,244
Рецензии: 366

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.

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76–100 из 355 отзывов о курсе Applied Social Network Analysis in Python

автор: Lutz H

19 июля 2019 г.

Great course! Really well explained with intuitive examples and great illustrations. At the end there is an interesting but challenging assignment.

автор: Devon H

5 мая 2018 г.

Great lecturer, comprehensive material and unlike other courses in this specialisation, actually prepares you well for the assignments and quizzes.

автор: Atilio T

22 мар. 2020 г.

Excellent course. The lecturer explains in a simple way to understand, and exercise are interested to the analysis of social network using python.

автор: Vincenzo T

16 мая 2019 г.

Very good course! I was afraid going into this after going the rather bad "Text Mining". However, it was super fun, well done and informative!

автор: Vladimir

29 дек. 2017 г.

A very good course to learn about networks. Thanks!

The cherry on top was to apply machine learning techniques to predict how the net evolves.

автор: Teo S

22 окт. 2017 г.

Best course in the series. The lecturer managed to explain difficult concepts very clearly through its excellent slides and words. Thank you!

автор: Vinicius d A O

16 мар. 2020 г.

This course was fantastic, with a lot of information and tips important for me. The instructor is very focused and I have confidence on him.

автор: Γεώργιος Κ

15 мая 2018 г.

Another must to have lesson from Michigan Univeristy. After completing this lesson the Social Networks will be an analysis challenge.

автор: Suyash D

19 дек. 2018 г.

An excellent course that provides a fair knowledge of social networks, the NetworkX package and how to work with networks in Python.

автор: Kueida L

3 сент. 2020 г.

The quizzes were not giving you free points like other online courses. They were challenging. The assignments were well-structured.

автор: Thales A K N

8 июля 2020 г.

Very cool knowledge!! Began the specialization not knowing that this kind of study existed, and it was awesome learning about it!!

автор: Henri

19 мая 2019 г.

Great intro to networks; last assignment is challenging but is a good opportunity to put everything together (python+ML+Network).


21 сент. 2020 г.

The concept and assignment are excellent .This lectures gives good idea about usage networkx . Overall the course is excellent .

автор: Elias

11 янв. 2018 г.

This is a very informative course in the property of networks and the feature extraction you can obtain out of this. Excellent

автор: Shiomar S C

5 нояб. 2019 г.

Excelente course, the instructor really meks you undestand with the right structure and having meaningfull in video quizes

автор: CasulGamer

11 июня 2019 г.

One of the best courses on social network analysis. Professor Daniel Romero did an excellent job explaining the contents.

автор: Varga I K

9 мар. 2019 г.

It was great introducing the networks, but I found most of the assignments too straightforward except for the last weeks.

автор: Mile D

20 дек. 2017 г.

Excellent explanations and examples. Recommended text to read was also very helpful. Thanks for providing this course!!!

автор: Sarah H H

3 июня 2019 г.

i found this course to be fun and straightforward. The assignments were directly aligned to instruction. Great course!

автор: Christian P

29 дек. 2019 г.

Excellent, well taught and in-depth programming exercises. I really got my hands into programming with networkx here.

автор: Oscar J O R

15 окт. 2017 г.

A really good course. Notebooks could be very useful to practice and maybe more exercises(not graded) with real data.

автор: Bhavraaj S

4 июня 2020 г.

But why am i not getting certififcate of completion.I need it with tthe course which holds more value than learning

автор: Slavisa D

9 июня 2019 г.

Very helpful, I didn't know anything about graphs, networks modelling and the NetworkX package before this course.

автор: Juan M

31 мая 2020 г.

Nice, delves on graph theory in quite an intuitive way, with exercises on Python. Can be recommended to a friend

автор: Yunhong H

23 мар. 2019 г.

Great course. The lectures are taught clearly. The knowledge gained in this course is very useful in real world.