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中級レベル

約16時間で修了

推奨:11 hours/week...

英語

字幕:英語, 韓国語

学習内容

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    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

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Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

約16時間で修了

推奨:11 hours/week...

英語

字幕:英語, 韓国語

シラバス - 本コースの学習内容

1
7時間で修了

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件のビデオ (合計48分), 3 readings, 2 quizzes
5件のビデオ
Bipartite Graphs12 分
TA Demonstration: Loading Graphs in NetworkX8 分
3件の学習用教材
Course Syllabus10 分
Help us learn more about you!10 分
Notice for Auditing Learners: Assignment Submission10 分
1の練習問題
Module 1 Quiz50 分
2
7時間で修了

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件のビデオ (合計55分), 2 quizzes
5件のビデオ
Network Robustness10 分
TA Demonstration: Simple Network Visualizations in NetworkX6 分
1の練習問題
Module 2 Quiz50 分
3
6時間で修了

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件のビデオ (合計70分), 2 quizzes
6件のビデオ
Scaled Page Rank8 分
Hubs and Authorities12 分
Centrality Examples8 分
1の練習問題
Module 3 Quiz50 分
4
9時間で修了

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件のビデオ (合計51分), 3 readings, 2 quizzes
3件の学習用教材
Power Laws and Rich-Get-Richer Phenomena (Optional)40 分
The Small-World Phenomenon (Optional)1 時間 20 分
Post-Course Survey10 分
1の練習問題
Module 4 Quiz50 分
4.7
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Applied Social Network Analysis in Python からの人気レビュー

by 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.

by 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.

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Daniel Romero

Assistant Professor
School of Information

ミシガン大学(University of Michigan)について

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....

Python 応用データサイエンスの専門講座について

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....
Python 応用データサイエンス

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