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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Angewandte Datenwissenschaft mit Python
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Spezialisierung Angewandte Datenwissenschaft mit PythonUniversity of MichiganÃœber diesen Kurs
53.199 kürzliche Aufrufe
Flexible Fristen
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Zertifikat zur Vorlage
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100Â % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 5 von 5 in
Stufe „Mittel“
Ca. 29 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Italienisch, Vietnamesisch, Koreanisch, Deutsch, Russisch, Englisch, Spanisch
Was Sie lernen werden
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
Kompetenzen, die Sie erwerben
- Graph Theory
- Network Analysis
- Python Programming
- Social Network Analysis
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100Â % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 5 von 5 in
Stufe „Mittel“
Ca. 29 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Italienisch, Vietnamesisch, Koreanisch, Deutsch, Russisch, Englisch, Spanisch
von
University of Michigan
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.
Lehrplan - Was Sie in diesem Kurs lernen werden
7 Stunden zum Abschließen
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.
7 Stunden zum Abschließen
5 Videos (Gesamt 48 min), 3 Lektüren, 2 Quiz
7 Stunden zum Abschließen
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.
7 Stunden zum Abschließen
5 Videos (Gesamt 55 min)
6 Stunden zum Abschließen
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 Stunden zum Abschließen
6 Videos (Gesamt 70 min)
9 Stunden zum Abschließen
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.
9 Stunden zum Abschließen
3 Videos (Gesamt 51 min), 4 Lektüren, 2 Quiz
Bewertungen
- 5 stars73,95Â %
- 4 stars20Â %
- 3 stars4,10Â %
- 2 stars1Â %
- 1 star0,92Â %
Top-Bewertungen von APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON
von SR27. März 2020
Very helpful courses. I was able to review and got much better at some things I already knew like data visualization and was able to explore some new areas like network analysis.
von JL23. Sep. 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.
von BL17. Apr. 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.
von NK2. Mai 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.
Ãœber den Spezialisierung Angewandte Datenwissenschaft mit Python
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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