课程信息
4.6
680 ratings
118 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....
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

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Intermediate Level

中级

Clock

建议:10 hours/week

完成时间大约为17 小时
Comment Dots

English

字幕:English, Korean

您将学到的内容有

  • 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

您将获得的技能

Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Intermediate Level

中级

Clock

建议:10 hours/week

完成时间大约为17 小时
Comment Dots

English

字幕:English, Korean

教学大纲 - 您将从这门课程中学到什么

1

章节
Clock
完成时间为 7 小时

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 个视频(共 48 分钟), 3 个阅读材料, 2 个测验
Video5 个视频
Network Definition and Vocabulary9分钟
Node and Edge Attributes9分钟
Bipartite Graphs12分钟
TA Demonstration: Loading Graphs in NetworkX8分钟
Reading3 个阅读材料
Course Syllabus10分钟
Help us learn more about you!10分钟
Notice for Auditing Learners: Assignment Submission10分钟
Quiz1 个练习
Module 1 Quiz50分钟

2

章节
Clock
完成时间为 7 小时

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 个视频(共 55 分钟), 2 个测验
Video5 个视频
Distance Measures17分钟
Connected Components9分钟
Network Robustness10分钟
TA Demonstration: Simple Network Visualizations in NetworkX6分钟
Quiz1 个练习
Module 2 Quiz50分钟

3

章节
Clock
完成时间为 6 小时

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 个视频(共 70 分钟), 2 个测验
Video6 个视频
Betweenness Centrality18分钟
Basic Page Rank9分钟
Scaled Page Rank8分钟
Hubs and Authorities12分钟
Centrality Examples8分钟
Quiz1 个练习
Module 3 Quiz50分钟

4

章节
Clock
完成时间为 9 小时

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 个视频(共 51 分钟), 3 个阅读材料, 2 个测验
Video3 个视频
Small World Networks19分钟
Link Prediction18分钟
Reading3 个阅读材料
Power Laws and Rich-Get-Richer Phenomena (Optional)40分钟
The Small-World Phenomenon (Optional)20分钟
Post-Course Survey10分钟
Quiz1 个练习
Module 4 Quiz50分钟
4.6
Direction Signs

47%

完成这些课程后已开始新的职业生涯
Briefcase

83%

通过此课程获得实实在在的工作福利
Money

30%

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热门审阅

创建者 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.

创建者 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.

讲师

Daniel Romero

Assistant Professor
School of Information

关于 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....

关于 Applied Data Science with Python 专项课程

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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