Acerca de este curso: 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.
Para quién es esta clase: 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.
Creada por: University of Michigan
Enseñado por: Daniel Romero, Assistant Professor
School of Information
| Información básica | Curso 5 de 5 en Applied Data Science with Python Specialization |
| Nivel | Intermedio |
| Idioma | English, Subtítulos: Korean |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 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 videos, 3 lecturas
- Reading: Course Syllabus
- Reading: Help us learn more about you!
- Vídeo: Network Definition and Vocabulary
- Vídeo: Node and Edge Attributes
- Vídeo: Bipartite Graphs
- Reading: Notice for Auditing Learners: Assignment Submission
- Notebook: Loading Graphs in NetworkX
- Vídeo: TA Demonstration: Loading Graphs in NetworkX
- Notebook: Assignment 1
Calificado: Module 1 Quiz
Calificado: Assignment 1 Submission
SEMANA 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 videos
- Vídeo: Distance Measures
- Vídeo: Connected Components
- Vídeo: Network Robustness
- Notebook: Simple Network Visualizations in NetworkX
- Vídeo: TA Demonstration: Simple Network Visualizations in NetworkX
- Notebook: Assignment 2
Calificado: Module 2 Quiz
Calificado: Assignment 2 Submission
SEMANA 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 videos
- Vídeo: Betweenness Centrality
- Vídeo: Basic Page Rank
- Vídeo: Scaled Page Rank
- Vídeo: Hubs and Authorities
- Vídeo: Centrality Examples
- Discussion Prompt: PageRank and Centrality in a real-life network
- Notebook: Assignment 3
Calificado: Module 3 Quiz
Calificado: Assignment 3 Submission
SEMANA 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 videos, 3 lecturas
- Reading: Power Laws and Rich-Get-Richer Phenomena (Optional)
- Vídeo: Small World Networks
- Vídeo: Link Prediction
- Notebook: Extracting Features from Graphs
- Reading: The Small-World Phenomenon (Optional)
- Notebook: Assignment 4
- Reading: Post-Course Survey
Calificado: Module 4 Quiz
Calificado: Assignment 4 Submission
Preguntas Frecuentes
Cómo funciona
Coursework
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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.
Tarifa
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
Calificaciones y revisiones
Excellent course and professor!
Remarkably good explanations, and interesting selection of subtopics. Interestingly , it does not delve into Facebook or any other social media applications, and is still just as valuable as it covers Graphs in some depth. Uses Python and its NetworkX library. Knowledge of classification models and scikit-learn is needed for the 4th assignment.
Highly valuable course and a good starter for network analysis. Do recommend!
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MV
in my opinon, the best of the specialization. thank you