Applied Social Network Analysis in Python
€0, aangeboden door Coursera
About this course: This course will introduce the learner to network modelling through the networkx toolset. Used to model knowledge graphs and physical and virtual networks, the lens will be social network analysis. The course begins with an understanding of what network modelling is (graph theory) and motivations for why we might model phenomena as networks. The second week introduces the networkx library and discusses how to build and visualize networks. The third week will describe metrics as they relate to the networks and demonstrate how these metrics can be applied to graph structures. The final week will explore the social networking analysis workflow, from problem identification through to generation of insight. This course is number 5 in the Applied Data Science with Python specialization. If you are enrolled in the specialization, 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.
Who is this class for: This course is part of the skills-based specialization “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Created by:Â Â Â University of Michigan
Taught by:Â Â Â Â Christopher Brooks, Research Assistant Professor
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Taught by:Â Â Â Â Kevyn Collins-Thompson, Associate Professor
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Taught by:Â Â Â Â Daniel Romero, Assistant Professor
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Taught by:Â Â Â Â V. G. Vinod Vydiswaran, Assistant Professor
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Basic Info
Course 5 of 5 in the Applied Data Science with Python Specialization.
Level
Intermediate
Language
English
How To Pass
Pass all graded assignments to complete the course.
Course 5 of Specialization
Gain new insights into your data. Learn to apply data science methods and techniques, and acquire analysis skills.
Applied Data Science with Python
University of Michigan
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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.
Syllabus