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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: 2,492
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

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:

326–350 из 410 отзывов о курсе Applied Social Network Analysis in Python

автор: bob n

22 сент. 2020 г.

Good basic course, well paced. I liked the instructor. Weekly assignments fair, some tougher than others. Occasionally finicky Auto grader a bit like artillery, need to send a couple of rounds over to home in on target.

автор: Devansh K

28 дек. 2020 г.

Extremely detailed and challenging course. The assignments require a lot of thinking and skill. Gives a comprehensive overview of social network analysis and a good way for any novice python coder to improve their skills

автор: Bernardo A

8 окт. 2017 г.

Really good overview of concepts and analysis related to 'graphs'. Could be more challenging when it comes to projects: for example, teach students to gather real data from twitter or facebook and make graphs with it.

автор: Chris M

7 окт. 2017 г.

I know its hard to go in deep detail with these courses. If you used one graph and gradually built upon it through the course it may reinforce the concepts better. Thoroughly enjoyed though, learned a lot.

автор: Chad A

13 янв. 2018 г.

The material and assignments were great and well aligned. The autograder for the Jupyter Notebooks was finicky at best and resulted in lots of time wasted getting formatting correct.

автор: Vivien A

16 мар. 2021 г.

Great content but assignment / auto grader sometimes difficult to deal with. In particular, errors not clearly described. Much time wasted due to wrong package version, etc. etc.

автор: Eric M

9 окт. 2017 г.

This was an excellent overview of using and analyzing graphs with Python. I learned a lot, got to apply my learning from previous courses, and I earned my Specialization!

автор: Raul M

6 июля 2018 г.

Great class for an introduction to networks.I didn't give it 5 stars because it didn't give me enough information to apply the concepts learned to real life projects.

автор: Vishal S

16 июля 2018 г.

Lectures are very well-designed. Especially, the assignment of week 4 is too good, that give me an overview of how we can apply machine learning in network analysis.

автор: Steffen H

20 нояб. 2018 г.

Course was ok, the assignments are not too difficult. I wish the course would provided more insights and discussions of the presented metrics of centrality though.

автор: Sean D

26 июня 2019 г.

Overall, good course. It could use more explicit examples of NetworkX in the actual Jupyter Notebook itself, but the coverage of the material is high quality.

автор: Ezequiel P

16 сент. 2020 г.

Great course! The topic is very interesting! I would have liked it to have more hands-on approach during the lectures, but the course quality is great

автор: YUJI H

28 июня 2018 г.

The presentation documents are very helpful to understand the lectures. If they can be downloaded to our local laptop, I evaluate this course 5 stars.

автор: Alejandro B

10 янв. 2020 г.

Great course, however, there is quite complicated the autograder system. Sometimes it takes too much time trying to figure out technical issues.

автор: Martin U

27 янв. 2019 г.

This was a great course, lots of great insights to gain. Only thing that was frustrating was the multiple choice quiz questions. I hated those.

автор: Tom M

4 нояб. 2017 г.

A bit confusing material since it is new to me. Lots of material in a short course. The auto grader is a bit difficult to work with.

автор: cadmium

16 апр. 2020 г.

The course provides a good overview of basic measures for network data. I took as prep for a harder course. I would recommend it.

автор: Dmitry B

14 сент. 2017 г.

This course was easier that the previous 4 in the specialization as it used them as a foundation for practical graph analysis.

автор: Victor G

31 окт. 2018 г.

Intreesting and rich in learning. The last assignment was specially fun. Would be nice with more such free assignments.

автор: Daniel D A

28 мар. 2020 г.

I liked the lectures but the assignments were significantly harder and had content that we didn't learn in the lecture

автор: Lucas G

21 сент. 2017 г.

Nice overview of general graph theory, and some useful exercises on how it can be applied for social network analysis.

автор: Mike W

20 нояб. 2019 г.

If you've had prior expose to graphs (e.g., an intermediate-level CS course), the first 2.5 weeks is pretty easy.

автор: Shashi T

17 нояб. 2018 г.

This was wonderful course in terms of content and content delivery. Prof was really nice. His pace was very good.

автор: Bart C

10 дек. 2018 г.

Great course! Love the instructor. Good background in networks, while sticking to the applied side of things.

автор: Juan V P

14 авг. 2019 г.

Good course with a nice and clean talk professor. Perhaps I miss some real-world cases in the assignments.