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.
Ce cours fait partie de la Spécialisation Applied Data Science with Python
Applied Social Network Analysis in Python
proposé par
À propos de ce cours
4.6
957 notes
Ce que vous allez apprendre
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
Compétences que vous acquerrez
Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
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 vidéos (Total 48 min), 3 lectures, 2 quiz
5 vidéos
Network Definition and Vocabulary9 min
Node and Edge Attributes9 min
Bipartite Graphs12 min
TA Demonstration: Loading Graphs in NetworkX8 min
3 lectures
Course Syllabus10 min
Help us learn more about you!10 min
Notice for Auditing Learners: Assignment Submission10 min
1 exercice pour s'entraîner
Module 1 Quiz50 min
Semaine
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 vidéos (Total 55 min), 2 quiz
5 vidéos
Clustering Coefficient12 min
Distance Measures17 min
Connected Components9 min
Network Robustness10 min
TA Demonstration: Simple Network Visualizations in NetworkX6 min
1 exercice pour s'entraîner
Module 2 Quiz50 min
Semaine
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 vidéos (Total 70 min), 2 quiz
6 vidéos
Betweenness Centrality18 min
Basic Page Rank9 min
Scaled Page Rank8 min
Hubs and Authorities12 min
Centrality Examples8 min
1 exercice pour s'entraîner
Module 3 Quiz50 min
Semaine
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 vidéos (Total 51 min), 3 lectures, 2 quiz
3 lectures
Power Laws and Rich-Get-Richer Phenomena (Optional)40 min
The Small-World Phenomenon (Optional)20 min
Post-Course Survey10 min
1 exercice pour s'entraîner
Module 4 Quiz50 min
4.6
166 avis36%
a commencé une nouvelle carrière après avoir terminé ces cours
40%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
29%
a obtenu une augmentation de salaire ou une promotion
Meilleurs avis
par 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.
par CG•Sep 18th 2017
Excellent tour through the basic terminology and key metrics of Graphs, with a lot of help from the networkX library that simplifies many, otherwise tough, tasks, calculations and processes.
Enseignant
Daniel Romero
Assistant ProfessorSchool of Information
À propos de Université du 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.
À propos de la Spécialisation Applied Data Science with Python
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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