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.
Ce cours fait partie de la Spécialisation Science des données appliquée avec Python
Applied Social Network Analysis in Python
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À propos de ce cours
68,344 consultations récentes
Résultats de carrière des étudiants
34%
ont commencé une nouvelle carrière après avoir terminé ce cours
37%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
23%
a obtenu une augmentation de salaire ou une promotion
Compétences que vous acquerrez
Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis
Résultats de carrière des étudiants
34%
ont commencé une nouvelle carrière après avoir terminé ce cours
37%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
23%
a obtenu une augmentation de salaire ou une promotion
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 5 sur 5 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 16 heures pour terminer
Recommandé : 12 hours/week
Anglais
Sous-titres : Anglais, Coréen
Programme du cours : ce que vous apprendrez dans ce cours
7 heures pour terminer
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.
7 heures pour terminer
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
7 heures pour terminer
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.
7 heures pour terminer
5 vidéos (Total 55 min)
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
6 heures pour terminer
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.
6 heures pour terminer
6 vidéos (Total 70 min)
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
9 heures pour terminer
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.
9 heures pour terminer
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)1h 20min
Post-Course Survey10 min
1 exercice pour s'entraîner
Module 4 Quiz50 min
Avis
4.7
Meilleurs avis pour APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON
par NKMay 2, 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.
par JLSep 23, 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 CGSep 17, 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.
par DSFeb 24, 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.
par BLApr 17, 2018
Really enjoyed the mathematical component of this course. It was fun to see how you could connect the graph theoretical components to the machine learning concepts from earlier courses.
par CAJan 12, 2018
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.
par SRMar 27, 2020
Very helpful courses. I was able to review and got much better at some things I already knew like data visualization and was able to explore some new areas like network analysis.
par NPOct 7, 2017
Interesting material and easy to follow. Assignments and quizzes were sufficiently challenging, but not too difficult that I spent entire weekends troubleshooting my code.
Offert par
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 du Spécialisation Science des données appliquée avec 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.
Foire Aux Questions
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Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
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Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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