Acerca de este Curso
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Nivel intermedio

Aprox. 16 horas para completar

Sugerido: 11 hours/week...

Inglés (English)

Subtítulos: Inglés (English), Coreano

Qué aprenderás

  • Check

    Analyze the connectivity of a network

  • Check

    Measure the importance or centrality of a node in a network

  • Check

    Predict the evolution of networks over time

  • Check

    Represent and manipulate networked data using the NetworkX library

Habilidades que obtendrás

Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 16 horas para completar

Sugerido: 11 hours/week...

Inglés (English)

Subtítulos: Inglés (English), Coreano

Programa - Qué aprenderás en este curso

Semana
1
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.

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5 videos (Total 48 minutos), 3 readings, 2 quizzes
5 videos
Bipartite Graphs12m
TA Demonstration: Loading Graphs in NetworkX8m
3 lecturas
Course Syllabus10m
Help us learn more about you!10m
Notice for Auditing Learners: Assignment Submission10m
1 ejercicios de práctica
Module 1 Quiz50m
Semana
2
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.

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5 videos (Total 55 minutos), 2 quizzes
5 videos
Network Robustness10m
TA Demonstration: Simple Network Visualizations in NetworkX6m
1 ejercicios de práctica
Module 2 Quiz50m
Semana
3
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.

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6 videos (Total 70 minutos), 2 quizzes
6 videos
Scaled Page Rank8m
Hubs and Authorities12m
Centrality Examples8m
1 ejercicios de práctica
Module 3 Quiz50m
Semana
4
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.

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3 videos (Total 51 minutos), 3 readings, 2 quizzes
3 lecturas
Power Laws and Rich-Get-Richer Phenomena (Optional)40m
The Small-World Phenomenon (Optional)1h 20m
Post-Course Survey10m
1 ejercicios de práctica
Module 4 Quiz50m
4.7
207 revisionesChevron Right

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consiguió un beneficio tangible en su carrera profesional gracias a este curso

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Principales revisiones sobre Applied Social Network Analysis in Python

por NKMay 3rd 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 JLSep 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.

Instructores

Avatar

Daniel Romero

Assistant Professor
School of Information

Acerca de 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....

Acerca del 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. 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....
Ciencias de los Datos Aplicada con Python

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