University of Michigan
Applied Social Network Analysis in Python
University of Michigan

Applied Social Network Analysis in Python

This course is part of Applied Data Science with Python Specialization

Taught in English

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Daniel Romero

Instructor: Daniel Romero

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Course

Gain insight into a topic and learn the fundamentals

4.6

(2,690 reviews)

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94%

Intermediate level
Some related experience required
26 hours (approximately)
Flexible schedule
Learn at your own pace
Prepare for a degree

What you'll learn

  • 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

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Assessments

4 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(2,690 reviews)

|

94%

Intermediate level
Some related experience required
26 hours (approximately)
Flexible schedule
Learn at your own pace
Prepare for a degree

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This course is part of the Applied Data Science with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

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.

What's included

5 videos3 readings1 quiz1 programming assignment2 ungraded labs

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.

What's included

5 videos1 quiz1 programming assignment1 ungraded lab

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.

What's included

6 videos1 quiz1 programming assignment1 discussion prompt

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.

What's included

3 videos5 readings1 quiz1 programming assignment1 ungraded lab

Instructor

Instructor ratings
4.7 (185 ratings)
Daniel Romero
University of Michigan
3 Courses101,991 learners

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