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.
Este curso faz parte do Programa de cursos integrados Ciência de dados aplicada com Python
Applied Social Network Analysis in Python
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Informações sobre o curso
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Resultados de carreira do aprendiz
34%
comecei uma nova carreira após concluir estes cursos
37%
consegui um benefício significativo de carreira com este curso
23%
recebi um aumento ou promoção
Habilidades que você terá
Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis
Resultados de carreira do aprendiz
34%
comecei uma nova carreira após concluir estes cursos
37%
consegui um benefício significativo de carreira com este curso
23%
recebi um aumento ou promoção
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 16 horas para completar
Sugerido: 12 hours/week
Inglês
Legendas: Inglês, Coreano
Programa - O que você aprenderá com este curso
7 horas para concluir
Why Study Networks and Basics on NetworkX
Module One introduces you to different types of networks in the real world and why we study them. You'll learn about the basic elements of networks, as well as different types of networks. You'll also learn how to represent and manipulate networked data using the NetworkX library. The assignment will give you an opportunity to use NetworkX to analyze a networked dataset of employees in a small company.
7 horas para concluir
5 vídeos (Total 48 mín.), 3 leituras, 2 testes
5 videos
Network Definition and Vocabulary9min
Node and Edge Attributes9min
Bipartite Graphs12min
TA Demonstration: Loading Graphs in NetworkX8min
3 leituras
Course Syllabus10min
Help us learn more about you!10min
Notice for Auditing Learners: Assignment Submission10min
1 exercício prático
Module 1 Quiz50min
7 horas para concluir
Network Connectivity
In Module Two you'll learn how to analyze the connectivity of a network based on measures of distance, reachability, and redundancy of paths between nodes. In the assignment, you will practice using NetworkX to compute measures of connectivity of a network of email communication among the employees of a mid-size manufacturing company.
7 horas para concluir
5 vídeos (Total 55 mín.)
5 videos
Distance Measures17min
Connected Components9min
Network Robustness10min
TA Demonstration: Simple Network Visualizations in NetworkX6min
1 exercício prático
Module 2 Quiz50min
6 horas para concluir
Influence Measures and Network Centralization
In Module Three, you'll explore ways of measuring the importance or centrality of a node in a network, using measures such as Degree, Closeness, and Betweenness centrality, Page Rank, and Hubs and Authorities. You'll learn about the assumptions each measure makes, the algorithms we can use to compute them, and the different functions available on NetworkX to measure centrality. In the assignment, you'll practice choosing the most appropriate centrality measure on a real-world setting.
6 horas para concluir
6 vídeos (Total 70 mín.)
6 videos
Betweenness Centrality18min
Basic Page Rank9min
Scaled Page Rank8min
Hubs and Authorities12min
Centrality Examples8min
1 exercício prático
Module 3 Quiz50min
9 horas para concluir
Network Evolution
In Module Four, you'll explore the evolution of networks over time, including the different models that generate networks with realistic features, such as the Preferential Attachment Model and Small World Networks. You will also explore the link prediction problem, where you will learn useful features that can predict whether a pair of disconnected nodes will be connected in the future. In the assignment, you will be challenged to identify which model generated a given network. Additionally, you will have the opportunity to combine different concepts of the course by predicting the salary, position, and future connections of the employees of a company using their logs of email exchanges.
9 horas para concluir
3 vídeos (Total 51 mín.), 3 leituras, 2 testes
3 leituras
Power Laws and Rich-Get-Richer Phenomena (Optional)40min
The Small-World Phenomenon (Optional)1h 20min
Post-Course Survey10min
1 exercício prático
Module 4 Quiz50min
Avaliações
Principais avaliações do APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON
por NKMay 2, 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.
por JLSep 23, 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.
por CGSep 17, 2017
Excellent tour through the basic terminology and key metrics of Graphs, with a lot of help from the networkX library that simplifies many, otherwise tough, tasks, calculations and processes.
por BLApr 17, 2018
Really enjoyed the mathematical component of this course. It was fun to see how you could connect the graph theoretical components to the machine learning concepts from earlier courses.
por DSFeb 24, 2018
I loved this course. It was well taught and had excellent problem sets and quizzes to internalize the learning. The material is very relevant to the market today. I highly recommend it.
por CAJan 12, 2018
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.
por SRMar 27, 2020
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.
por NPOct 7, 2017
Interesting material and easy to follow. Assignments and quizzes were sufficiently challenging, but not too difficult that I spent entire weekends troubleshooting my code.
por EMOct 8, 2017
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!
por FLNov 22, 2017
Very good class.\n\nThe lecturer is amazing!! The quizzes help you understand the concepts. The assignments are a little basic though.\n\nOverall you learn a great deal.
por KASep 26, 2017
It's rare to find an amazing course in network analysis online, and I'm very glad to have taken this course and learn the art of network analysis for research purposes.
por SPNov 17, 2017
This course contains many important concepts of Graph Theory and Network Analysis. The explanation is clear and neat. Also, the assignments are fun and comprehensible.
por RMJul 5, 2018
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.
por VSJul 15, 2018
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.
por SSFeb 18, 2018
Very comprehensive course for introduction of social network analysis. Best part is every concept is covered in detail and how to implement using networkx library.
por SHNov 20, 2018
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.
por JSFeb 26, 2018
Great explanations. The instructor is awesome and has good visual material. In-video quizzes keep you engaged during the lecture. I am very happy with the course.
por NSJun 1, 2019
I found it hard sometimes to understand the concepts but this gave me quite an introduction on social network analysis and encouraged me to learn more about them.
por JDDec 14, 2019
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 .
por SDJun 25, 2019
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.
Sobre Universidade de Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
Sobre Programa de cursos integrados Ciência de dados aplicada com Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
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