에 포함됨This 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.
제공자:
Python과 함께하는 응용 데이터 과학 특화 과정미시건 대학교이 강좌에 대하여
최근 조회 56,036회
학습자 경력 결과
34%
가 이 강좌를 수료한 후 새로운 커리어를 시작함
37%
가 이 강좌를 통해 확실한 경력상 이점을 얻음
23%
가 급여 인상 또는 승진 성취
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 5개 강좌 중 5번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 29시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 이탈리아어, 베트남어, 한국어, 독일어, 러시아어, 영어, 스페인어
배울 내용
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
귀하가 습득할 기술
Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis
학습자 경력 결과
34%
가 이 강좌를 수료한 후 새로운 커리어를 시작함
37%
가 이 강좌를 통해 확실한 경력상 이점을 얻음
23%
가 급여 인상 또는 승진 성취
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 5개 강좌 중 5번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 29시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 이탈리아어, 베트남어, 한국어, 독일어, 러시아어, 영어, 스페인어
제공자:
미시건 대학교
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.
강의 계획 - 이 강좌에서 배울 내용
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.
완료하는 데 7시간 필요
5개 동영상 (총 48분), 3 개의 읽기 자료, 2 개의 테스트
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.
완료하는 데 7시간 필요
5개 동영상 (총 55분)
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.
완료하는 데 6시간 필요
6개 동영상 (총 70분)
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.
완료하는 데 9시간 필요
3개 동영상 (총 51분), 4 개의 읽기 자료, 2 개의 테스트
검토
APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON의 최상위 리뷰
NK 제공2019년 5월 2일
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.
JL 제공2018년 9월 23일
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.
CG 제공2017년 9월 17일
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.
BL 제공2018년 4월 17일
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.
Python과 함께하는 응용 데이터 과학 특화 과정 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.
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