Mining Data from Networks

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Justin Flett

1:46:56

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01 - Course Overview

01 - Course Overview.mp4

01:49

02 - Understanding Networks

02 - Introduction.mp4

00:55

03 - Understanding Networks.mp4

04:09

04 - Network Components.mp4

02:38

05 - Implementing a Network with NetworkX.mp4

06:10

06 - Directed and Undirected Networks.mp4

02:53

07 - Summary.mp4

01:21

03 - Visualizing Network Data

08 - Introduction.mp4

00:47

09 - Understanding Network Data Visualization.mp4

05:00

10 - Understanding Adjacency Matrices.mp4

06:48

11 - Implementing Adjacency Matrices.mp4

02:26

12 - Understanding and Implementing Incidence Matrices.mp4

03:14

13 - Additional Methods for Network Visualization.mp4

02:13

14 - Summary.mp4

01:31

04 - Defining and Identifying Network Data

15 - Introduction.mp4

01:27

16 - Understanding Neighborhoods and Shortest Paths.mp4

04:50

17 - Implementing Neighborhoods and Shortest Paths.mp4

05:27

18 - Understanding Network Centrality.mp4

06:43

19 - Implementing Network Centrality.mp4

03:57

20 - Understanding Cliques and Clusters.mp4

04:50

21 - Implementing Cliques and Clusters.mp4

02:54

22 - Summary.mp4

01:59

05 - Implementing Link Prediction Models for Network Analysis

23 - Introduction.mp4

00:57

24 - Understanding Link Prediction.mp4

02:43

25 - Link Prediction with Common Neighbors.mp4

04:49

26 - Link Prediction with Jaccard Coefficient.mp4

04:09

27 - Link Prediction with Preferential Attachment.mp4

03:49

28 - Summary.mp4

01:09

06 - Implementing Collaborative Filtering Models for Network Analysis

29 - Introduction.mp4

01:08

30 - Understanding Collaborative Filtering.mp4

05:24

31 - Implementing Collaborative Filtering.mp4

05:03

32 - Further Learning and Next Steps.mp4

01:43

33 - Summary.mp4

02:01

Description

NetworkX is a widely-used data science and machine learning software library. This course will teach you the basics of implementing network analysis using NetworkX, including visualization, link prediction, and collaborative filtering systems.

What You'll Learn?

Data Science and Machine Learning are rapidly growing fields that use scientific methods and processes to extract useful knowledge and insights from data. In this course, Mining Data from Networks, you will learn foundational knowledge of solving real world data science problems. First, you will learn the basics of implementing network analysis including understanding and visualizing network data. Next, you will discover how to define and identify important network data using NetworkX and Python. Finally, you will explore understanding and implementing link prediction and collaborative filtering systems. When you’re finished with this course, you will have the skills and knowledge of NetworkX needed to solve data science and machine learning problems.

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Computer Network

Network Engineering

Justin Flett

Instructor's Courses

Justin Flett is a Mechatronics Engineer currently working as a Professor within the Faculty of Applied Science and Technology at Sheridan College. Justin has previously held positions at Hydro One Networks, Ford Motor Company, and ABB Robotics spanning across both the electrical and mechanical engineering industries. Most recently, he has been working as an Product Development Professional specializing in training, services, and consultation nation-wide, ranging from design fundamentals to advanced product development solutions.

Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.