About this Course
60,475

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 17시간 필요

권장: 11 hours/week...

영어

자막: 영어, 한국어

배울 내용

  • 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

귀하가 습득할 기술

Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 17시간 필요

권장: 11 hours/week...

영어

자막: 영어, 한국어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 7시간 필요

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 (Total 48 min), 3 readings, 2 quizzes
5개의 동영상
Network Definition and Vocabulary9m
Node and Edge Attributes9m
Bipartite Graphs12m
TA Demonstration: Loading Graphs in NetworkX8m
3개의 읽기 자료
Course Syllabus10m
Help us learn more about you!10m
Notice for Auditing Learners: Assignment Submission10m
1개 연습문제
Module 1 Quiz50m
2
완료하는 데 7시간 필요

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 (Total 55 min), 2 quizzes
5개의 동영상
Distance Measures17m
Connected Components9m
Network Robustness10m
TA Demonstration: Simple Network Visualizations in NetworkX6m
1개 연습문제
Module 2 Quiz50m
3
완료하는 데 6시간 필요

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 (Total 70 min), 2 quizzes
6개의 동영상
Betweenness Centrality18m
Basic Page Rank9m
Scaled Page Rank8m
Hubs and Authorities12m
Centrality Examples8m
1개 연습문제
Module 3 Quiz50m
4
완료하는 데 9시간 필요

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 (Total 51 min), 3 readings, 2 quizzes
3개의 동영상
Small World Networks19m
Link Prediction18m
3개의 읽기 자료
Power Laws and Rich-Get-Richer Phenomena (Optional)40m
The Small-World Phenomenon (Optional)20m
Post-Course Survey10m
1개 연습문제
Module 4 Quiz50m
4.6
190개의 리뷰Chevron Right

38%

이 강좌를 수료한 후 새로운 경력 시작하기

39%

이 강좌를 통해 확실한 경력상 이점 얻기

26%

급여 인상 또는 승진하기

최상위 리뷰

대학: 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.

대학: 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.

강사

Avatar

Daniel Romero

Assistant Professor
School of Information

미시건 대학교 정보

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....

Python과 함께하는 응용 데이터 과학 전문 분야 정보

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....
Python과 함께하는 응용 데이터 과학

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.