Informações sobre o curso
4.6
839 classificações
149 avaliações
Programa de cursos integrados
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Nível intermediário

Nível intermediário

Horas para completar

Aprox. 17 horas para completar

Sugerido: 11 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês, Coreano

O que você vai aprender

  • Check

    Analyze the connectivity of a network

  • Check

    Measure the importance or centrality of a node in a network

  • Check

    Predict the evolution of networks over time

  • Check

    Represent and manipulate networked data using the NetworkX library

Habilidades que você terá

Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis
Programa de cursos integrados
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Nível intermediário

Nível intermediário

Horas para completar

Aprox. 17 horas para completar

Sugerido: 11 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês, Coreano

Programa - O que você aprenderá com este curso

Semana
1
Horas para completar
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....
Reading
5 videos (Total 48 min), 3 leituras, 2 testes
Video5 videos
Network Definition and Vocabulary9min
Node and Edge Attributes9min
Bipartite Graphs12min
TA Demonstration: Loading Graphs in NetworkX8min
Reading3 leituras
Course Syllabus10min
Help us learn more about you!10min
Notice for Auditing Learners: Assignment Submission10min
Quiz1 exercício prático
Module 1 Quiz50min
Semana
2
Horas para completar
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. ...
Reading
5 videos (Total 55 min), 2 testes
Video5 videos
Distance Measures17min
Connected Components9min
Network Robustness10min
TA Demonstration: Simple Network Visualizations in NetworkX6min
Quiz1 exercício prático
Module 2 Quiz50min
Semana
3
Horas para completar
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....
Reading
6 videos (Total 70 min), 2 testes
Video6 videos
Betweenness Centrality18min
Basic Page Rank9min
Scaled Page Rank8min
Hubs and Authorities12min
Centrality Examples8min
Quiz1 exercício prático
Module 3 Quiz50min
Semana
4
Horas para completar
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. ...
Reading
3 videos (Total 51 min), 3 leituras, 2 testes
Video3 videos
Small World Networks19min
Link Prediction18min
Reading3 leituras
Power Laws and Rich-Get-Richer Phenomena (Optional)40min
The Small-World Phenomenon (Optional)20min
Post-Course Survey10min
Quiz1 exercício prático
Module 4 Quiz50min
4.6
149 avaliaçõesChevron Right
Direcionamento de carreira

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Benefício de carreira

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Promoção de carreira

30%

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Melhores avaliações

por JLSep 24th 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 18th 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.

Instrutores

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Daniel Romero

Assistant Professor
School of Information

Sobre University of 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 o Programa de cursos integrados Applied Data Science with 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....
Applied Data Science with Python

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