Course Summary
Learn how to analyze social networks using Python! This course will introduce the basics of network analysis and provide hands-on experience working with real-life network data.Key Learning Points
- Understand the basics of network analysis
- Learn how to work with different network data formats
- Apply network analysis techniques to real-life data
Related Topics for further study
Learning Outcomes
- Understand the basics of network analysis and its applications
- Learn how to manipulate and analyze network data using Python
- Apply network analysis techniques to real-life data and draw insights
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with data analysis concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Hands-on exercises
Similar Courses
- Social Network Analysis
- Applied Data Science with Python
- Graph Analytics for Big Data
Related Education Paths
Related Books
Description
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.
Knowledge
- 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
Outline
- Why Study Networks and Basics on NetworkX
- Networks: Definition and Why We Study Them
- Network Definition and Vocabulary
- Node and Edge Attributes
- Bipartite Graphs
- TA Demonstration: Loading Graphs in NetworkX
- Course Syllabus
- Help us learn more about you!
- Notice for Auditing Learners: Assignment Submission
- Module 1 Quiz
- Network Connectivity
- Clustering Coefficient
- Distance Measures
- Connected Components
- Network Robustness
- TA Demonstration: Simple Network Visualizations in NetworkX
- Module 2 Quiz
- Influence Measures and Network Centralization
- Degree and Closeness Centrality
- Betweenness Centrality
- Basic Page Rank
- Scaled Page Rank
- Hubs and Authorities
- Centrality Examples
- Module 3 Quiz
- Network Evolution
- Preferential Attachment Model
- Small World Networks
- Link Prediction
- Power Laws and Rich-Get-Richer Phenomena (Optional)
- The Small-World Phenomenon (Optional)
- Post-Course Survey
- Keep Learning with Michigan Online!
- Module 4 Quiz
Summary of User Reviews
Discover the power of Python for Social Network Analysis with this online course on Coursera. This course has received positive reviews for its comprehensive coverage of the topic and its practical approach to learning.Key Aspect Users Liked About This Course
Many users appreciated the course's emphasis on practical applications of social network analysis.Pros from User Reviews
- Comprehensive coverage of social network analysis
- Practical approach to learning
- Engaging and knowledgeable instructors
- Interactive assignments and quizzes
- Great for beginners and advanced learners alike
Cons from User Reviews
- Some users found the pace of the course to be too slow
- The course may require some prior programming knowledge
- Limited opportunities for hands-on learning outside of assignments
- Some users found the course to be too theoretical at times
- No certificate of completion for the audit option
Daniel Romero
4.7 Raiting
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