About this Course
4.6
844 ratings
151 reviews
Specialization
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100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 17 hours to complete

Suggested: 11 hours/week...
Available languages

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

Graph TheoryNetwork AnalysisPython ProgrammingSocial Network Analysis
Specialization
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 17 hours to complete

Suggested: 11 hours/week...
Available languages

English

Subtitles: English, Korean

Syllabus - What you will learn from this course

Week
1
Hours to complete
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 exercise
Module 1 Quiz50m
Week
2
Hours to complete
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 exercise
Module 2 Quiz50m
Week
3
Hours to complete
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 exercise
Module 3 Quiz50m
Week
4
Hours to complete
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 exercise
Module 4 Quiz50m
4.6
151 ReviewsChevron Right
Career direction

47%

started a new career after completing these courses
Career Benefit

83%

got a tangible career benefit from this course
Career promotion

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Top Reviews

By JLSep 24th 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.

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.

Instructor

Avatar

Daniel Romero

Assistant Professor
School of Information

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....

About the Applied Data Science with Python Specialization

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....
Applied Data Science with Python

Frequently Asked Questions

  • 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.

  • 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.

More questions? Visit the Learner Help Center.