课程信息
59,393 次近期查看

第 5 门课程(共 5 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

完成时间大约为16 小时

建议:11 hours/week...

英语(English)

字幕:英语(English), 韩语

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

第 5 门课程(共 5 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

完成时间大约为16 小时

建议:11 hours/week...

英语(English)

字幕:英语(English), 韩语

学习Course的学生是

  • Data Scientists
  • Data Analysts
  • Machine Learning Engineers
  • User Experience Researchers
  • Security Engineers

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 7 小时

Why Study Networks and Basics on NetworkX

5 个视频 (总计 48 分钟), 3 个阅读材料, 2 个测验
5 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
3 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
1 个练习
Module 1 Quiz50分钟
2
完成时间为 7 小时

Network Connectivity

5 个视频 (总计 55 分钟), 2 个测验
5 个视频
Distance Measures17分钟
Connected Components9分钟
Network Robustness10分钟
TA Demonstration: Simple Network Visualizations in NetworkX6分钟
1 个练习
Module 2 Quiz50分钟
3
完成时间为 6 小时

Influence Measures and Network Centralization

6 个视频 (总计 70 分钟), 2 个测验
6 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
1 个练习
Module 3 Quiz50分钟
4
完成时间为 9 小时

Network Evolution

3 个视频 (总计 51 分钟), 3 个阅读材料, 2 个测验
3 个视频
Small World Networks19分钟
Link Prediction18分钟
3 个阅读材料
Power Laws and Rich-Get-Richer Phenomena (Optional)40分钟
The Small-World Phenomenon (Optional)1 小时 20 分
Post-Course Survey10分钟
1 个练习
Module 4 Quiz50分钟
4.6
225 个审阅Chevron Right

34%

完成这些课程后已开始新的职业生涯

37%

通过此课程获得实实在在的工作福利

23%

加薪或升职

来自Applied Social Network Analysis in Python的热门评论

创建者 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.

创建者 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.

讲师

Avatar

Daniel Romero

Assistant Professor
School of Information

关于 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....

关于 借助 Python 应用数据科学 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....
借助 Python 应用数据科学

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