Informações sobre o curso
4.6
609 ratings
110 reviews
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....
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Intermediate Level

Nível intermediário

Clock

Sugerido: 10 hours/week

Aprox. 17 horas restantes
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English

Legendas: English, Korean

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

Network AnalysisSocial Network AnalysisPython ProgrammingGraph Theory
Globe

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Calendar

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Intermediate Level

Nível intermediário

Clock

Sugerido: 10 hours/week

Aprox. 17 horas restantes
Comment Dots

English

Legendas: English, Korean

Programa - O que você aprenderá com este curso

1

Seção
Clock
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 vídeos (Total de 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

2

Seção
Clock
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 vídeos (Total de 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

3

Seção
Clock
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 vídeos (Total de 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

4

Seção
Clock
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 vídeos (Total de 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
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Melhores avaliações

por DSFeb 25th 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 BLApr 18th 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.

Instrutores

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 basic a 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

Perguntas Frequentes – FAQ

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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