Об этом курсе
4.6
672 ratings
118 reviews
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....
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Intermediate Level

Промежуточный уровень

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Предполагаемая нагрузка: 10 hours/week

Прибл. 17 ч. на завершение
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English

Субтитры: English, Korean

Чему вы научитесь

  • 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

Приобретаемые навыки

Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis
Globe

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Intermediate Level

Промежуточный уровень

Clock

Предполагаемая нагрузка: 10 hours/week

Прибл. 17 ч. на завершение
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English

Субтитры: English, Korean

Программа курса: что вы изучите

1

Раздел
Clock
7 ч. на завершение

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....
Reading
5 видео (всего 48 мин.), 3 материалов для самостоятельного изучения, 2 тестов
Video5 видео
Network Definition and Vocabulary9мин
Node and Edge Attributes9мин
Bipartite Graphs12мин
TA Demonstration: Loading Graphs in NetworkX8мин
Reading3 материала для самостоятельного изучения
Course Syllabus10мин
Help us learn more about you!10мин
Notice for Auditing Learners: Assignment Submission10мин
Quiz1 практическое упражнение
Module 1 Quiz50мин

2

Раздел
Clock
7 ч. на завершение

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. ...
Reading
5 видео (всего 55 мин.), 2 тестов
Video5 видео
Distance Measures17мин
Connected Components9мин
Network Robustness10мин
TA Demonstration: Simple Network Visualizations in NetworkX6мин
Quiz1 практическое упражнение
Module 2 Quiz50мин

3

Раздел
Clock
6 ч. на завершение

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....
Reading
6 видео (всего 70 мин.), 2 тестов
Video6 видео
Betweenness Centrality18мин
Basic Page Rank9мин
Scaled Page Rank8мин
Hubs and Authorities12мин
Centrality Examples8мин
Quiz1 практическое упражнение
Module 3 Quiz50мин

4

Раздел
Clock
9 ч. на завершение

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. ...
Reading
3 видео (всего 51 мин.), 3 материалов для самостоятельного изучения, 2 тестов
Video3 видео
Small World Networks19мин
Link Prediction18мин
Reading3 материала для самостоятельного изучения
Power Laws and Rich-Get-Richer Phenomena (Optional)40мин
The Small-World Phenomenon (Optional)20мин
Post-Course Survey10мин
Quiz1 практическое упражнение
Module 4 Quiz50мин
4.6
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Briefcase

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получил значимые преимущества в карьере благодаря этому курсу
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Лучшие рецензии

автор: CGSep 18th 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.

автор: DSFeb 25th 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.

Преподаватель

Daniel Romero

Assistant Professor
School of Information

О 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....

О специализации ''Applied Data Science with 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....
Applied Data Science with Python

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