Company: Pluralsight Author: Artur Krochin Full Title: Network Analysis In Python: Getting Started Year: 2019 Language: English Genre: Educational: Networking Skill Level: Beginner Price: - - Files: MP4 (+ Code Files, Slides .PDF) Time: 01:58:26 Video: AVC, 1280 x 720 (1.778) at 30.000 fps, 200 kbps Audio: AAC at 96 Kbps, 2 channels, 44.1 KHz Network science is an underutilized part of data science. This course will empower you to leverage the network data your company has. You'll learn about network wrangling and visualization, centralities, communities, and machine learning techniques. Companies have amassed terabytes of data that can be represented as networks. However, due to a lack of data professionals skilled in network methods, this data is being underutilized. The aim of this course is to fix that and empower you to be able to reason about and build products based on networks. In this course, Network Analysis in Python: Getting Started, you'll gain the foundational skills needed to analyze networks using Python. First, you'll learn about the origins of network science and its relation to graph theory, as well as practical skills in manipulating graphs in NetworkX. Next, you'll explore how to create beautiful and illustrative visualizations of networks using the native capabilities of NetworkX and Bokeh. Then, you'll deep dive into centrality and community detection algorithms. Finally, you'll enrich your machine learning toolbox by learning about network embeddings. By the end of the course, you'll have learned how to conduct your own analysis of networks, how to visualize networks, and even how to build an advanced friendship prediction engine using network science and machine learning. Lessons: 1. Course Overview 01. Course Overview 2. Introducing NetworkX and Network Science 02. Module Outline 03. Course Prerequisites 04. Network Science vs. Graph Theory 05. What Is NetworkX? 06. Manipulating Networks in NetworkX 07. Graph Theory through NetworkX 08. Accessing and Modifying Attributes 09. Graph Storage Formats 3. Analyzing Networks Visually 10. Module Outline 11. Why Network Visualization? 12. Native Visualization in NetworkX 13. Introduction to Bokeh 14. Bokeh: Plots and Tools 15. Bokeh: Visualizing Node Attributes 16. A Primer on Visual Network Analysis 4. Calculating Centralities and Detecting Communities with NetworkX 17. Module Outline 18. Why Centrality Measures? 19. Degree Centrality 20. Closeness Centrality 21. Betweenness Centrality 22. Katz, Eigenvector, and PageRank Centralities 23. Community Detection: Girvan-Newman Algorithm 24. Demo: Detecting Communities in NetworkX 5. Aiding Machine Learning with Network Science 25. Module Outline 26. Motivating Embeddings 27. Word2vec 28. Node2vec 29. Demo: Node2vec Our members see more. Join us! ------------- Our members see more. Join us!
