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
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Programa de cursos integrados Ciência de dados aplicada com PythonUniversidade de MichiganInformações sobre o curso
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Nível intermediário
Aprox. 29 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Coreano, Alemão, Russo, Inglês, Espanhol
O que você vai aprender
Represent and manipulate networked data using the NetworkX library
Analyze the connectivity of a network
Measure the importance or centrality of a node in a network
Predict the evolution of networks over time
Habilidades que você terá
- Graph Theory
- Network Analysis
- Python Programming
- Social Network Analysis
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível intermediário
Aprox. 29 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Coreano, Alemão, Russo, Inglês, Espanhol
oferecido por
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.
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
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.)
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.)
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.), 4 leituras, 2 testes
Avaliações
- 5 stars73,93%
- 4 stars20,04%
- 3 stars4,07%
- 2 stars1,01%
- 1 star0,93%
Principais avaliações do APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON
por VS15 de jul de 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 SR27 de mar de 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 SP17 de nov de 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 FA22 de nov de 2020
Great introductory course on graph theory using Networkx. The instructor goes through each algorithm with step-by-step examples, and gives relevant examples at the end of each topic.
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.
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