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.
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課程信息
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完成這些課程後已開始新的職業生涯
37%
通過此課程獲得實實在在的工作福利
23%
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您將獲得的技能
Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis
學生職業成果
34%
完成這些課程後已開始新的職業生涯
37%
通過此課程獲得實實在在的工作福利
23%
加薪或升職
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100% 在線
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第 5 門課程（共 5 門）
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中級
完成時間大約為16 小時
建議：12 hours/week
英語（English）
字幕：英語（English）, 韓語
提供方
密歇根大学
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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 7 小時
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.
完成時間為 7 小時
5 個視頻 （總計 48 分鐘）, 3 個閱讀材料, 2 個測驗
5 個視頻
Network Definition and Vocabulary9分鐘
Node and Edge Attributes9分鐘
Bipartite Graphs12分鐘
TA Demonstration: Loading Graphs in NetworkX8分鐘
3 個閱讀材料
Course Syllabus10分鐘
Help us learn more about you!10分鐘
Notice for Auditing Learners: Assignment Submission10分鐘
1 個練習
Module 1 Quiz50分鐘
完成時間為 7 小時
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.
完成時間為 7 小時
5 個視頻 （總計 55 分鐘）
5 個視頻
Distance Measures17分鐘
Connected Components9分鐘
Network Robustness10分鐘
TA Demonstration: Simple Network Visualizations in NetworkX6分鐘
1 個練習
Module 2 Quiz50分鐘
完成時間為 6 小時
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.
完成時間為 6 小時
6 個視頻 （總計 70 分鐘）
6 個視頻
Betweenness Centrality18分鐘
Basic Page Rank9分鐘
Scaled Page Rank8分鐘
Hubs and Authorities12分鐘
Centrality Examples8分鐘
1 個練習
Module 3 Quiz50分鐘
完成時間為 9 小時
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.
完成時間為 9 小時
3 個視頻 （總計 51 分鐘）, 3 個閱讀材料, 2 個測驗
3 個閱讀材料
Power Laws and Rich-Get-Richer Phenomena (Optional)40分鐘
The Small-World Phenomenon (Optional)1 小時 20 分
Post-Course Survey10分鐘
1 個練習
Module 4 Quiz50分鐘
審閱
4.7
來自APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON的熱門評論
由 NK 提供May 2, 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.
由 JL 提供Sep 23, 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.
由 CG 提供Sep 17, 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.
由 DS 提供Feb 24, 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.
由 BL 提供Apr 17, 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.
由 CA 提供Jan 12, 2018
The material and assignments were great and well aligned. The autograder for the Jupyter Notebooks was finicky at best and resulted in lots of time wasted getting formatting correct.
由 NP 提供Oct 7, 2017
Interesting material and easy to follow. Assignments and quizzes were sufficiently challenging, but not too difficult that I spent entire weekends troubleshooting my code.
由 EM 提供Oct 8, 2017
This was an excellent overview of using and analyzing graphs with Python. I learned a lot, got to apply my learning from previous courses, and I earned my Specialization!
由 FL 提供Nov 22, 2017
Very good class.\n\nThe lecturer is amazing!! The quizzes help you understand the concepts. The assignments are a little basic though.\n\nOverall you learn a great deal.
由 KA 提供Sep 26, 2017
It's rare to find an amazing course in network analysis online, and I'm very glad to have taken this course and learn the art of network analysis for research purposes.
由 SP 提供Nov 17, 2017
This course contains many important concepts of Graph Theory and Network Analysis. The explanation is clear and neat. Also, the assignments are fun and comprehensible.
由 RM 提供Jul 5, 2018
Great class for an introduction to networks.I didn't give it 5 stars because it didn't give me enough information to apply the concepts learned to real life projects.
由 VS 提供Jul 15, 2018
Lectures are very well-designed. Especially, the assignment of week 4 is too good, that give me an overview of how we can apply machine learning in network analysis.
由 SS 提供Feb 18, 2018
Very comprehensive course for introduction of social network analysis. Best part is every concept is covered in detail and how to implement using networkx library.
由 SH 提供Nov 20, 2018
Course was ok, the assignments are not too difficult. I wish the course would provided more insights and discussions of the presented metrics of centrality though.
由 NS 提供Jun 1, 2019
I found it hard sometimes to understand the concepts but this gave me quite an introduction on social network analysis and encouraged me to learn more about them.
由 JS 提供Feb 26, 2018
Great explanations. The instructor is awesome and has good visual material. In-video quizzes keep you engaged during the lecture. I am very happy with the course.
由 JD 提供Dec 14, 2019
Really helpful. Get a basic idea of the social network and how to use python to analyze it. Will definitely dig deeper and see how it could relate to my work .
由 SD 提供Jun 25, 2019
Overall, good course. It could use more explicit examples of NetworkX in the actual Jupyter Notebook itself, but the coverage of the material is high quality.
由 PV 提供Jan 28, 2020
All over the course is very relevant to what is a need in industry. Very nice video lectures, to the point and crisp. Material is quite informative too.
關於 借助 Python 应用数据科学 專項課程
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.
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