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.
This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
本課程是 Applied Data Science with Python 專項課程 專項課程的一部分
Applied Social Network Analysis in Python
提供方
課程信息
4.6
791 個評分
您將學到的內容有
Analyze the connectivity of a network
Measure the importance or centrality of a node in a network
Predict the evolution of networks over time
Represent and manipulate networked data using the NetworkX library
您將獲得的技能
Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis
教學大綱 - 您將從這門課程中學到什麼
週
1Why 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.
5 個視頻（共 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
週
2Network 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.
5 個視頻（共 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
週
3Influence 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.
6 個視頻（共 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
週
4Network 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.
3 個視頻（共 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)20分鐘
Post-Course Survey10分鐘
1 個練習
Module 4 Quiz50分鐘
4.6
138 個審閱47%
83%
30%
熱門審閱
創建者 JL•Sep 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 DS•Feb 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
Daniel Romero
Assistant ProfessorSchool 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.
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