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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第 5 門課程（共 5 門）
中級
完成時間大約為29 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
您將學到的內容有
Represent and manipulate networked data using the NetworkX library
Analyze the connectivity of a network
Measure the importance or centrality of a node in a network
Predict the evolution of networks over time
您將獲得的技能
- Graph Theory
- Network Analysis
- Python Programming
- Social Network Analysis
可靈活調整截止日期
根據您的日程表重置截止日期。
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）
中級
完成時間大約為29 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
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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.
授課大綱 - 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
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）
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）
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）, 4 個閱讀材料, 2 個測驗
審閱
- 5 stars73.93%
- 4 stars20.04%
- 3 stars4.07%
- 2 stars1.01%
- 1 star0.93%
來自APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON的熱門評論
由 CA 提供2018年1月12日
The material and assignments were great and well aligned. The autograder for the Jupyter Notebooks was finicky at best and resulted in lots of time wasted getting formatting correct.
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.
由 RM 提供2018年7月5日
Great class for an introduction to networks.I didn't give it 5 stars because it didn't give me enough information to apply the concepts learned to real life projects.
由 NS 提供2019年6月1日
I found it hard sometimes to understand the concepts but this gave me quite an introduction on social network analysis and encouraged me to learn more about them.
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.
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