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.
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課程信息
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學生職業成果
34%
完成這些課程後已開始新的職業生涯
37%
通過此課程獲得實實在在的工作福利
23%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 5 門課程（共 5 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
中級
完成時間大約為29 小時
英語（English）
字幕：英語（English）, 韓語
您將獲得的技能
Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis
學生職業成果
34%
完成這些課程後已開始新的職業生涯
37%
通過此課程獲得實實在在的工作福利
23%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 5 門課程（共 5 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
中級
完成時間大約為29 小時
英語（English）
字幕：英語（English）, 韓語
提供方
密歇根大学
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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 7 小時
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.
完成時間為 7 小時
5 個視頻 （總計 48 分鐘）, 3 個閱讀材料, 2 個測驗
5 個視頻
Network Definition and Vocabulary9分鐘
Node and Edge Attributes9分鐘
Bipartite Graphs12分鐘
TA Demonstration: Loading Graphs in NetworkX8分鐘
3 個閱讀材料
Course Syllabus10分鐘
Help us learn more about you!10分鐘
Notice for Auditing Learners: Assignment Submission10分鐘
1 個練習
Module 1 Quiz50分鐘
完成時間為 7 小時
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.
完成時間為 7 小時
5 個視頻 （總計 55 分鐘）
5 個視頻
Distance Measures17分鐘
Connected Components9分鐘
Network Robustness10分鐘
TA Demonstration: Simple Network Visualizations in NetworkX6分鐘
1 個練習
Module 2 Quiz50分鐘
完成時間為 6 小時
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.
完成時間為 6 小時
6 個視頻 （總計 70 分鐘）
6 個視頻
Betweenness Centrality18分鐘
Basic Page Rank9分鐘
Scaled Page Rank8分鐘
Hubs and Authorities12分鐘
Centrality Examples8分鐘
1 個練習
Module 3 Quiz50分鐘
完成時間為 9 小時
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.
完成時間為 9 小時
3 個視頻 （總計 51 分鐘）, 3 個閱讀材料, 2 個測驗
3 個閱讀材料
Power Laws and Rich-Get-Richer Phenomena (Optional)40分鐘
The Small-World Phenomenon (Optional)1 小時 20 分
Post-Course Survey10分鐘
1 個練習
Module 4 Quiz50分鐘
審閱
來自APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON的熱門評論
由 NK 提供2019年5月2日
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.
由 JL 提供2018年9月23日
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.
由 CG 提供2017年9月17日
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.
由 DS 提供2018年2月24日
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.
關於 借助 Python 应用数据科学 專項課程
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.
常見問題
我什么时候能够访问课程视频和作业？
讲座和作业的访问权限取决于您的注册类型。如果您以旁听模式参加课程，则可以免费查看大多数课程资料。要访问评分作业并获得证书，您需要在旁听期间或之后购买证书体验。如果看不到旁听选项：
- 课程可能不提供旁听选项。您可以尝试免费试用，也可以申请助学金。
- 课程可能会改为提供'完整课程，没有证书'。通过此选项，您可以查看所有课程材料、提交所要求的作业，以及获得最终成绩。这也意味着您将无法购买证书体验。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
Is financial aid available?
是的，Coursera 可以为无法承担费用的学生提供助学金。通过点击左侧“注册”按钮下的“助学金”链接可以申请助学金。您可以根据屏幕提示完成申请，申请获批后会收到通知。您需要针对专项课程中的每一门课程完成上述步骤，包括毕业项目。了解更多。
還有其他問題嗎？請訪問 學生幫助中心。
