This course is part of the Applied Data Science with Python Specialization

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Daniel Romero

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Applied Data Science with Python SpecializationUniversity of Michigan

About this Course

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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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Course 5 of 5 in the

Applied Data Science with Python Specialization

Intermediate Level

Approx. 26 hours to complete

English

What you will 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

Skills you will gain

Graph Theory
Network Analysis
Python Programming
Social Network Analysis

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 5 of 5 in the

Applied Data Science with Python Specialization

Intermediate Level

Approx. 26 hours to complete

English

Instructor

Instructor rating

4.74/5 (178 Ratings)

Daniel Romero

Assistant Professor

School of Information

94,446 Learners

3 Courses

Offered by

University of Michigan

Start working towards your Master's degree

This course is related to the 100% online Master of Applied Data Science from University of Michigan. It will provide you with a preview of the topics, materials and instructors so you can decide if the full online degree program is right for you.

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Syllabus - What you will learn from this course

Content Rating94%(3,082 ratings)

Week1

Week 1

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.

7 hours to complete

5 videos (Total 48 min), 3 readings, 2 quizzes

Week2

Week 2

6 hours to complete

Network Connectivity

6 hours to complete

5 videos (Total 55 min)

Week3

Week 3

5 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.

5 hours to complete

6 videos (Total 69 min)

Week4

Week 4

8 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.

8 hours to complete

3 videos (Total 51 min), 4 readings, 2 quizzes

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TOP REVIEWS FROM APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON

by SSFeb 18, 2018

Very comprehensive course for introduction of social network analysis. Best part is every concept is covered in detail and how to implement using networkx library.

by TPNov 15, 2022

I really like the content of the course.

What needs to improve is the networkx package is used in the lab. It's an old version with old functions and they are not working sometime.

by DSFeb 24, 2018

I loved this course. It was well taught and had excellent problem sets and quizzes to internalize the learning. The material is very relevant to the market today. I highly recommend it.

by NKMay 2, 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.

View all reviews

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.

Frequently Asked Questions

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What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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Applied Social Network Analysis in Python

About this Course

What you will learn

Skills you will gain