本课程是 Applied Data Science with Python 专项课程 专项课程的一部分

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Applied Data Science with Python 专项课程

University of Michigan

课程信息

4.6

680 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....

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

完成时间大约为17 小时

字幕：English, Korean

- Analyze the connectivity of a network
- Measure the importance or centrality of a node in a network
- Predict the evolution of networks over time
- Represent and manipulate networked data using the NetworkX library

Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

完成时间大约为17 小时

字幕：English, Korean

章节

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 个视频（共 48 分钟）, 3 个阅读材料, 2 个测验

Network Definition and Vocabulary9分钟

Node and Edge Attributes9分钟

Bipartite Graphs12分钟

TA Demonstration: Loading Graphs in NetworkX8分钟

Course Syllabus10分钟

Help us learn more about you!10分钟

Notice for Auditing Learners: Assignment Submission10分钟

Module 1 Quiz50分钟

章节

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 个视频（共 55 分钟）, 2 个测验

Distance Measures17分钟

Connected Components9分钟

Network Robustness10分钟

TA Demonstration: Simple Network Visualizations in NetworkX6分钟

Module 2 Quiz50分钟

章节

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 个视频（共 70 分钟）, 2 个测验

Betweenness Centrality18分钟

Basic Page Rank9分钟

Scaled Page Rank8分钟

Hubs and Authorities12分钟

Centrality Examples8分钟

Module 3 Quiz50分钟

章节

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 个视频（共 51 分钟）, 3 个阅读材料, 2 个测验

Power Laws and Rich-Get-Richer Phenomena (Optional)40分钟

The Small-World Phenomenon (Optional)20分钟

Post-Course Survey10分钟

Module 4 Quiz50分钟

4.6

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创建者 CG•Sep 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.

创建者 BL•Apr 18th 2018

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.

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....

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....

When will I have access to the lectures and assignments?

Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

What is the refund policy?

Is financial aid available?

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