About this Course
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100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 16 hours to complete

Suggested: 11 hours/week...

English

Subtitles: English, Korean
User
Learners taking this Course are
  • Data Scientists
  • Data Analysts
  • User Experience Researchers
  • Machine Learning Engineers
  • Data Engineers

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
User
Learners taking this Course are
  • Data Scientists
  • Data Analysts
  • User Experience Researchers
  • Machine Learning Engineers
  • Data Engineers

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 16 hours to complete

Suggested: 11 hours/week...

English

Subtitles: English, Korean

Syllabus - What you will learn from this course

Week
1
7 hours to complete

Why Study Networks and Basics on NetworkX

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

Network Connectivity

5 videos (Total 55 min), 2 quizzes
5 videos
Distance Measures17m
Connected Components9m
Network Robustness10m
TA Demonstration: Simple Network Visualizations in NetworkX6m
1 practice exercise
Module 2 Quiz50m
Week
3
6 hours to complete

Influence Measures and Network Centralization

6 videos (Total 70 min), 2 quizzes
6 videos
Betweenness Centrality18m
Basic Page Rank9m
Scaled Page Rank8m
Hubs and Authorities12m
Centrality Examples8m
1 practice exercise
Module 3 Quiz50m
Week
4
9 hours to complete

Network Evolution

3 videos (Total 51 min), 3 readings, 2 quizzes
3 videos
Small World Networks19m
Link Prediction18m
3 readings
Power Laws and Rich-Get-Richer Phenomena (Optional)40m
The Small-World Phenomenon (Optional)1h 20m
Post-Course Survey10m
1 practice exercise
Module 4 Quiz50m
4.6
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Top reviews from Applied Social Network Analysis in Python

By NKMay 3rd 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.

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.

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.