Daniel Romero
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.
Instructor Details
Courses : 1
Specification: Applied Social Network Analysis in Python
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58 reviews for Applied Social Network Analysis in Python
Add a review
| Price | Free |
|---|---|
| Provider | |
| Duration | 16 hours |
| Year | 2017 |
| Level | Intermediate |
| Language | English ... |
| Certificate | Yes |
| Quizzes | Yes |
FREE
John H E O –
Amazing course!!!
Daniel R –
My favourite course
Shadi A –
Great course
Harshith S –
Daniel Romero is probably the best instructor in this specialization
Sean D –
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.
Ruihua G –
this course provided a overview of the network analysis.
Jose H C –
Interesting.!
Lutz H –
Great course! Really well explained with intuitive examples and great illustrations. At the end there is an interesting but challenging assignment.
Dirisala S –
The have lot of stuff to learn. It will definitely enhance your skill.
JATIN G –
nice learning
Shwetank A –
nice course
TEJASWI S –
Good course
Avi R –
Satisfactory
Ahmad H S –
it is good but we are looking for more real practices
Ryan D –
The specialization for Applied Data science started strong, with engaging exercises, good instruction, and good recommendations for additional reading and resources. As the specialization continued, the courses seemed to get “lazy”, and the course topics became more abstract and less applied. After going through this specialization, I would not recommend this to someone if I could find a better program through edX or another coursera offering.
Kevin c –
For a coding heavy course, why doesn’t the instructor just upload the code used in slides as a Jupyter Notebook? This would save A LOT OF TIME and frustration. Right now, I have to pause the video to copy the code AND write my own notes and it wastes so much time. Not to mention, you can easily be prone to writing wrong syntax when you’re trying to keep up so fast, and then you run the code chunk and it doesn’t work and you have to go back to that point in the video. It’s a simple staple that I would have expected in a UMich course. Also, they don’t show how to create networks from pre existing data, which is how you will usually work in the real world
Juan V P –
Good course with a nice and clean talk professor. Perhaps I miss some real world cases in the assignments.
Saurabh M –
An excellent course
Igor K –
Nice course, worth to listen to
Tian L –
a great introductory course.
Renzo B –
I learned a lot of things that I can apply to my line of work.
Amila R –
Good starting point for those who want ro learn social network analysis.
Chethan S L –
Excellent
KRISHNASAI R –
the very best course it is very helpful and useful
Dongquan S –
Very well organized course. Thank you!
Shiomar S C –
Excelente course, the instructor really meks you undestand with the right structure and having meaningfull in video quizes
Suleman k –
Great for Beginners
Steven G –
Excellent course. Interesting content and well taught.
John A C –
I loved learning all about graph theory!
Mike W –
If you’ve had prior expose to graphs (e.g., an intermediate level CS course), the first 2.5 weeks is pretty easy.
Wenlei Y –
This course really opens my eye, providing a new standpoint from which we visualize “network”.
EDILSON S S O J –
Perfect Course! Exactly what I was looking for to deep my understanding in Graph Theory and Practice!
Nishal –
Good information, at a good pace, explained very well
Natasha D –
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.
Praveen R –
I learnt about networkx and its capabilities. The course introduces to many network algorithms and talks about concepts of centrality, page rank, etc. Good eye opener to all these concepts. The last assignment is very practical and challenging. Enjoyed the course. Praveen
Jiaqi d –
Really helpful. Get a basic idea of the social network and how to use python to analyze it. Will definitely dig deeper and see how it could relate to my work .
sonam a –
not interesting.
Minshen C –
it would be great if some case study of prediction can be added to the course
Christian P –
Excellent, well taught and in depth programming exercises. I really got my hands into programming with networkx here.
Alejandro B –
Great course, however, there is quite complicated the autograder system. Sometimes it takes too much time trying to figure out technical issues.
Vinit D –
Tough course
Behzad M –
Very interesting, I have learnt a lot.
Gurami K –
it was a great course!
Hassan N –
thank’s so much
MUHAMMAD M M –
Good!
Piyush V –
All over the course is very relevant to what is a need in industry. Very nice video lectures, to the point and crisp. Material is quite informative too.
Haris P D –
One of the most awesome course that I have taken on Coursera!
Hewawitharanage A H –
good
Eric W –
Clear, concise, well organised and structured.
Carlos F P –
The course provides a great introduction to graph analytics, I consider that the social network applications are very sparse or missing in action altogether. Nonetheless, overall great content and practice of extracting information from networks with Python.
Atilio T –
Excellent course. The lecturer explains in a simple way to understand, and exercise are interested to the analysis of social network using python.
Pranjal J –
Excellent Course
akash p –
it was helpful
Tatek K –
Excellent presentation, exercise and reading materials. Thank you
Akash D –
Thank You! Sir
Spencer R –
Very helpful courses. I was able to review and got much better at some things I already knew like data visualization and was able to explore some new areas like network analysis.
Daniel B d A A –
I liked the lectures but the assignments were significantly harder and had content that we didn’t learn in the lecture
Su L –
enjoyed it very much, thank you Professor and mentors