Applied Social Network Analysis in Python
$79
ENROLL NOWCourse Overview
What You'll Learn
- 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.
- 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.
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.
Course FAQs
Is this an accredited online course?
Accreditation for 'Applied Social Network Analysis in Python' is determined by the provider, University of Michigan. For online college courses or degree programs, we strongly recommend you verify the accreditation status directly on the provider's website to ensure it meets your requirements.
Can this course be used for continuing education credits?
Many of the courses listed on our platform are suitable for professional continuing education. However, acceptance for credit varies by state and licensing board. Please confirm with your board and {course.provider} that this specific course qualifies.
How do I enroll in this online school program?
To enroll, click the 'ENROLL NOW' button on this page. You will be taken to the official page for 'Applied Social Network Analysis in Python' on the University of Michigan online class platform, where you can complete your registration.
