Applied Social Network Analysis in Python

4.6
495 ratings
95 reviews

Course 5 of 5 in the Applied Data Science with Python Specialization

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.
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100% online course

Start instantly and learn at your own schedule.
Intermediate Level

Intermediate Level

Clock

Approx. 18 hours to complete

Suggested: 10 hours/week
Comment Dots

English

Subtitles: English, Korean

What you will learn

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

Skills you will gain

Network AnalysisSocial Network AnalysisPython ProgrammingMachine Learning
Globe

100% online course

Start instantly and learn at your own schedule.
Intermediate Level

Intermediate Level

Clock

Approx. 18 hours to complete

Suggested: 10 hours/week
Comment Dots

English

Subtitles: English, Korean

Syllabus - What you will learn from this course

1

Section
Clock
7 hours to complete

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....
Reading
5 videos (Total 48 min), 3 readings, 2 quizzes
Video5 videos
Network Definition and Vocabulary9m
Node and Edge Attributes9m
Bipartite Graphs12m
TA Demonstration: Loading Graphs in NetworkX8m
Reading3 readings
Course Syllabus10m
Help us learn more about you!10m
Notice for Auditing Learners: Assignment Submission10m
Quiz1 practice exercises
Module 1 Quiz50m

2

Section
Clock
7 hours to complete

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. ...
Reading
5 videos (Total 55 min), 2 quizzes
Video5 videos
Distance Measures17m
Connected Components9m
Network Robustness10m
TA Demonstration: Simple Network Visualizations in NetworkX6m
Quiz1 practice exercises
Module 2 Quiz50m

3

Section
Clock
6 hours to complete

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....
Reading
6 videos (Total 70 min), 2 quizzes
Video6 videos
Betweenness Centrality18m
Basic Page Rank9m
Scaled Page Rank8m
Hubs and Authorities12m
Centrality Examples8m
Quiz1 practice exercises
Module 3 Quiz50m

4

Section
Clock
9 hours to complete

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. ...
Reading
3 videos (Total 51 min), 3 readings, 2 quizzes
Video3 videos
Small World Networks19m
Link Prediction18m
Reading3 readings
Power Laws and Rich-Get-Richer Phenomena (Optional)40m
The Small-World Phenomenon (Optional)20m
Post-Course Survey10m
Quiz1 practice exercises
Module 4 Quiz50m
4.6
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47%

started a new career after completing these courses
Briefcase

83%

got a tangible career benefit from this course
Money

30%

got a pay increase or promotion

Top Reviews

By CGSep 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.

By BLApr 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.

Instructor

Avatar

Daniel Romero

Assistant Professor

About 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....

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