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.
Este curso forma parte de Programa especializado: Ciencias de los Datos Aplicada con Python
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Programa especializado: Ciencias de los Datos Aplicada con PythonUniversidad de MíchiganAcerca de este Curso
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Fechas límite flexibles
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Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
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Nivel intermedio
Aprox. 29 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
Qué aprenderás
Represent and manipulate networked data using the NetworkX library
Analyze the connectivity of a network
Measure the importance or centrality of a node in a network
Predict the evolution of networks over time
Habilidades que obtendrás
- Graph Theory
- Network Analysis
- Python Programming
- Social Network Analysis
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Aprox. 29 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
Universidad de Míchigan
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.
Programa - Qué aprenderás en este curso
7 horas para completar
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 horas para completar
5 videos (Total 48 minutos), 4 lecturas, 2 cuestionarios
7 horas para completar
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 horas para completar
5 videos (Total 55 minutos)
6 horas para completar
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 horas para completar
6 videos (Total 70 minutos)
9 horas para completar
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 horas para completar
3 videos (Total 51 minutos), 4 lecturas, 2 cuestionarios
Reseñas
Principales reseñas sobre APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON
por NK2 de may. de 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.
por JL23 de sep. de 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.
por CG17 de sep. de 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.
por BL17 de abr. de 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.
Acerca de Programa especializado: Ciencias de los Datos Aplicada con 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.
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