課程信息
4.6
791 個評分
138 個審閱
專項課程

第 5 門課程(共 5 門),位於

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中級

中級

完成時間(小時)

完成時間大約為17 小時

建議:11 hours/week...
可選語言

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
專項課程

第 5 門課程(共 5 門),位於

100% online

100% online

立即開始,按照自己的計劃學習。
可靈活調整截止日期

可靈活調整截止日期

根據您的日程表重置截止日期。
中級

中級

完成時間(小時)

完成時間大約為17 小時

建議:11 hours/week...
可選語言

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(小時)
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....
Reading
5 個視頻(共 48 分鐘), 3 個閱讀材料, 2 個測驗
Video5 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
Reading3 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
Quiz1 個練習
Module 1 Quiz50分鐘
2
完成時間(小時)
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. ...
Reading
5 個視頻(共 55 分鐘), 2 個測驗
Video5 個視頻
Distance Measures17分鐘
Connected Components9分鐘
Network Robustness10分鐘
TA Demonstration: Simple Network Visualizations in NetworkX6分鐘
Quiz1 個練習
Module 2 Quiz50分鐘
3
完成時間(小時)
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....
Reading
6 個視頻(共 70 分鐘), 2 個測驗
Video6 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
Quiz1 個練習
Module 3 Quiz50分鐘
4
完成時間(小時)
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. ...
Reading
3 個視頻(共 51 分鐘), 3 個閱讀材料, 2 個測驗
Video3 個視頻
Small World Networks19分鐘
Link Prediction18分鐘
Reading3 個閱讀材料
Power Laws and Rich-Get-Richer Phenomena (Optional)40分鐘
The Small-World Phenomenon (Optional)20分鐘
Post-Course Survey10分鐘
Quiz1 個練習
Module 4 Quiz50分鐘
4.6
138 個審閱Chevron Right
職業方向

47%

完成這些課程後已開始新的職業生涯
工作福利

83%

通過此課程獲得實實在在的工作福利
職業晉升

30%

加薪或升職

熱門審閱

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.

創建者 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.

講師

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Daniel Romero

Assistant Professor
School of Information

關於 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....

關於 Applied Data Science with Python 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....
Applied Data Science with Python

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