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 should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
Who is this class for: This course is part of “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.
Taught by: Daniel Romero, Assistant Professor
School of Information
| 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. |
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