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.

curso 100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Nível intermediário

Aprox. 18 horas restantes

Sugerido: 10 hours/week

English

Legendas: English, Korean

O que você vai aprender

Analyze the connectivity of a network

Measure the importance or centrality of a node in a network

Predict the evolution of networks over time

Represent and manipulate networked data using the NetworkX library

Habilidades que você terá

Network AnalysisSocial Network AnalysisPython ProgrammingMachine Learning

curso 100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Nível intermediário

Aprox. 18 horas restantes

Sugerido: 10 hours/week

English

Legendas: English, Korean

Syllabus - What you will learn from this course

1

Section

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....

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

5 videos

Networks: Definition and Why We Study Them7m

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 exercises

Module 1 Quiz50m

2

Section

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. ...

5 videos (Total 55 min), 2 quizzes

5 videos

Clustering Coefficient12m

Distance Measures17m

Connected Components9m

Network Robustness10m

TA Demonstration: Simple Network Visualizations in NetworkX6m

1 practice exercises

Module 2 Quiz50m

3

Section

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....

6 videos (Total 70 min), 2 quizzes

6 videos

Degree and Closeness Centrality12m

Betweenness Centrality18m

Basic Page Rank9m

Scaled Page Rank8m

Hubs and Authorities12m

Centrality Examples8m

1 practice exercises

Module 3 Quiz50m

4

Section

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. ...

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

3 videos

Preferential Attachment Model12m

Small World Networks19m

Link Prediction18m

3 readings

Power Laws and Rich-Get-Richer Phenomena (Optional)40m

The Small-World Phenomenon (Optional)20m

Post-Course Survey10m

1 practice exercises

Module 4 Quiz50m

Instructor

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....

Frequently Asked Questions

When will I have access to the lectures and assignments?
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.
What will I get if I pay for this course?
If you pay for this course, you will have access to all of the features and content you need to earn a Course Certificate. If you complete the course successfully, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. Note that the Course Certificate does not represent official academic credit from the partner institution offering the course.
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Applied Data Science with Python Specialization