À propos de ce cours : 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.
Les destinataires de ce cours : This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course in the specialization contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Créé par : Université du Michigan
Enseigné par : Daniel Romero, Assistant Professor
School of Information
| Informations de base | Cours 5 de 5 dans Applied Data Science with Python Specialization |
| Niveau | Intermediate |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
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.
5 vidéos, 3 lectures
- Reading: Course Syllabus
- Reading: Help us learn more about you!
- Vidéo: Networks: Definition and Why We Study Them
- Vidéo: Network Definition and Vocabulary
- Vidéo: Node and Edge Attributes
- Vidéo: Bipartite Graphs
- Reading: Notice for Auditing Learners: Assignment Submission
- Notebook: Loading Graphs in NetworkX
- Vidéo: TA Demonstration: Loading Graphs in NetworkX
- Notebook: Assignment 1
Noté: Module 1 Quiz
Noté: Assignment 1 Submission
SEMAINE 2
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.
5 vidéos
- Vidéo: Clustering Coefficient
- Vidéo: Distance Measures
- Vidéo: Connected Components
- Vidéo: Network Robustness
- Notebook: Simple Network Visualizations in NetworkX
- Vidéo: TA Demonstration: Simple Network Visualizations in NetworkX
- Notebook: Assignment 2
Noté: Module 2 Quiz
Noté: Assignment 2 Submission
SEMAINE 3
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 vidéos
- Vidéo: Degree and Closeness Centrality
- Vidéo: Betweenness Centrality
- Vidéo: Basic Page Rank
- Vidéo: Scaled Page Rank
- Vidéo: Hubs and Authorities
- Vidéo: Centrality Examples
- Discussion Prompt: PageRank and Centrality in a real-life network
- Notebook: Assignment 3
Noté: Module 3 Quiz
Noté: Assignment 3 Submission
SEMAINE 4
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.
3 vidéos, 3 lectures
- Vidéo: Preferential Attachment Model
- Reading: Power Laws and Rich-Get-Richer Phenomena (Optional)
- Vidéo: Small World Networks
- Vidéo: Link Prediction
- Notebook: Extracting Features from Graphs
- Reading: The Small-World Phenomenon (Optional)
- Notebook: Assignment 4
- Reading: Post-Course Survey
Noté: Module 4 Quiz
Noté: Assignment 4 Submission
FAQ
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Coursework
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Créateurs
Université du Michigan
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Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
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.
I learned many interesting new concepts in social network analysis and a bunch of new graph algorithms, which are rarely taught in the "traditional" algorithm course. Now I know how companies like Cambridge Analytics can use the Facebook's social network data to derive useful information. (It's actually quite easy.) A class like this is more important than ever. I just wish we could have more time to explore a few topics more deeply.
Very clear explanation.
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JW
Well put together. Quizzes test on material covered and assignments expand on it. There is still challenge and rigor, but it comes from understanding the concepts, not ambiguity and lack of instruction. This is one of the best online courses I've taken.