課程信息
83,152 次近期查看

第 5 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

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16 小時

建議:11 hours/week...

英語(English)

字幕:英語(English), 韓語

您將學到的內容有

  • 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

您將獲得的技能

Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis

第 5 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

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16 小時

建議:11 hours/week...

英語(English)

字幕:英語(English), 韓語

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 7 小時

Why Study Networks and Basics on NetworkX

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
2
完成時間為 7 小時

Network Connectivity

5 個視頻 (總計 55 分鐘), 2 個測驗
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
3
完成時間為 6 小時

Influence Measures and Network Centralization

6 個視頻 (總計 70 分鐘), 2 個測驗
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
4
完成時間為 9 小時

Network Evolution

3 個視頻 (總計 51 分鐘), 3 個閱讀材料, 2 個測驗
3 個視頻
Small World Networks19分鐘
Link Prediction18分鐘
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
4.6
230 個審閱Chevron Right

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

來自Applied Social Network Analysis in Python的熱門評論

創建者 NKMay 3rd 2019

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.

創建者 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.

講師

Avatar

Daniel Romero

Assistant Professor
School of Information

關於 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....

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. 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....
借助 Python 应用数据科学

常見問題

  • 注册以便获得证书后,您将有权访问所有视频、测验和编程作业(如果适用)。只有在您的班次开课之后,才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程,可能无法访问某些作业。

  • 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。

還有其他問題嗎?請訪問 學生幫助中心