PLU | Network Analysis In Python: Getting Started (2019 EN)

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  1. Kanka

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    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.

    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