Learn Graphs and Social Network Analytics Using Python

Learn Graphs and Social Network Analytics Using Python, available at Free, has an average rating of 4.15, with 66 lectures, 3 quizzes, based on 611 reviews, and has 36302 subscribers.

You will learn about Create graphs using NetworkX package Create nodes of a graph Create edges of a graph Determine the attributes of a node and edges Analyze social networks like Facebook and Twitter Students will learn more about properties of a graph Learn about Clustering coefficient , Betweenness centrality, degree centrality etc Learn about Connected graphs, Bipartite graphs, etc Learn about the types of graphs used for social network analysis This course is ideal for individuals who are Beginners who have never programmed in python before or Students who are Graph Enthusiast or Intermediate python programmers who want to level up their skills or Students who want to analyze social networks like Facebook and Twitter or Mathematics students who wants to apply their knowledge in Graph Theory It is particularly useful for Beginners who have never programmed in python before or Students who are Graph Enthusiast or Intermediate python programmers who want to level up their skills or Students who want to analyze social networks like Facebook and Twitter or Mathematics students who wants to apply their knowledge in Graph Theory.

Enroll now: Learn Graphs and Social Network Analytics Using Python

Summary

Title: Learn Graphs and Social Network Analytics Using Python

Price: Free

Average Rating: 4.15

Number of Lectures: 66

Number of Quizzes: 3

Number of Published Lectures: 66

Number of Published Quizzes: 3

Number of Curriculum Items: 69

Number of Published Curriculum Objects: 69

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Create graphs using NetworkX package
Create nodes of a graph
Create edges of a graph
Determine the attributes of a node and edges
Analyze social networks like Facebook and Twitter
Students will learn more about properties of a graph
Learn about Clustering coefficient , Betweenness centrality, degree centrality etc
Learn about Connected graphs, Bipartite graphs, etc
Learn about the types of graphs used for social network analysis

Who Should Attend

Beginners who have never programmed in python before
Students who are Graph Enthusiast
Intermediate python programmers who want to level up their skills
Students who want to analyze social networks like Facebook and Twitter
Mathematics students who wants to apply their knowledge in Graph Theory

Target Audiences

Beginners who have never programmed in python before
Students who are Graph Enthusiast
Intermediate python programmers who want to level up their skills
Students who want to analyze social networks like Facebook and Twitter
Mathematics students who wants to apply their knowledge in Graph Theory

BRAND NEW COURSE IS HERE ! Learn Graphs and Social Network Analytics .Become a graph and social analyst today. This is a comprehensive course , simple and straight forward for python enthusiast and those with little python background. You want to learn about how to draw graphs and analyze them, this is the course for you. This course will contain some quizzes, test and some homework assignments, as well as some real world assignment projects. There is over 55 lectures and about 6hours to complete the course. This course comes with live coding screenshots using iPython Notebook .Below is the list of the course summary

– Overivew of networkX

– Install networkX module and iPython Notebooks

– Create nodes

– Add edges to nodes

– Getting attributes from a graph

– Manipulate your graphs ie.; remove nodes /edges

– Create DiGraphs/MultiGraphs/MultiDiGraphs

– Graph Generators

– Graph metrics ; shortest path/clustering coefficient

– Define functions

– Visualize graphs

– Calculate nodes/degree/centrality metrics

– Some random graphs

– Small famous graphs

– Reading and writing graph files

– Social network analysis

– Subgraphs

– Facebook Social Network Analysis

Course goals :

-At the end of the course students should be able to learn some basics of graph theory

– Students should be able to analyze Facebook social networks

– Students should take the simple quizzes

– Students should know what is directed and undirected graphs

– Students should be able to visualize graphs using different graph plots

– You can use this course to analyze the world as a network

– Everything in this world is now connected

– Extract useful information from graphs

Life time access to the course. What are you waiting for? Learn practical graph and social network analytics today that would improve your career and increase your knowledge.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Intro

Lecture 2: Github Account

Chapter 2: Overview of networkX

Lecture 1: Overview of networkX

Lecture 2: NetworkX Basics

Chapter 3: Installation of networkX and iPython Notebooks

Lecture 1: Installation of networkX and iPython Notebooks

Chapter 4: Creating nodes using networkX

Lecture 1: Creating Nodes using networkX

Chapter 5: Adding edges to graphs

Lecture 1: Adding edges to graphs

Chapter 6: Getting graph properties

Lecture 1: Getting graph properties

Chapter 7: Node Manipulation

Lecture 1: Node manipulation

Chapter 8: Adding attributes to graphs

Lecture 1: Adding attributes to graphs-01

Lecture 2: Adding attributes to graphs-02

Chapter 9: Adding edge attributes to graphs

Lecture 1: Adding edge attributes to graphs-01

Lecture 2: Adding edge attributes to graphs-02

Chapter 10: Creating DiGraphs

Lecture 1: Creating DiGraphs-01

Lecture 2: Creating DiGraphs-02

Chapter 11: Creating MultiGraphs

Lecture 1: Creating MultiGraphs

Chapter 12: Creating MultiDiGraphs

Lecture 1: Creating MultiDiGraphs

Chapter 13: Graph Generators

Lecture 1: Graph generators-01

Lecture 2: Graph generators-02

Chapter 14: Graph Metrics

Lecture 1: Shortest Path

Lecture 2: Clustering Coefficient

Chapter 15: Defining Functions

Lecture 1: Define functions to draw graphs-01

Lecture 2: Define functions to draw graphs-02

Lecture 3: Create nodes using a custom function

Lecture 4: Delete nodes using a custom function

Lecture 5: Delete edges using a custom function

Lecture 6: Custom node size and node color using a custom function

Lecture 7: Custom edge colors using a custom function

Chapter 16: Graph Visualizations

Lecture 1: Draw Images using networkX

Lecture 2: Draw circular graphs

Lecture 3: Draw bar graph using betweenness centrality

Chapter 17: Nodes , Degrees and Centrality Metrics

Lecture 1: Nodes, Degrees and Centrality

Chapter 18: Random Graphs

Lecture 1: Grid Graphs

Lecture 2: Circular Trees

Lecture 3: Bipartite Graphs

Lecture 4: Some random graphs

Lecture 5: House Graph

Chapter 19: Small Famous Graphs

Lecture 1: Small famous graphs

Lecture 2: Famous social network graphs

Lecture 3: Classical graphs

Chapter 20: Reading and writing graph files

Lecture 1: Writing files

Lecture 2: Reading files

Lecture 3: Writing edgeList graphs

Lecture 4: Reading graphs files using open function

Lecture 5: Reading edgeList graphs

Chapter 21: Social Network Analysis

Lecture 1: Social network -00

Lecture 2: Social network -01

Lecture 3: Social network -02

Lecture 4: Social network -03

Lecture 5: Social network -04

Lecture 6: Social network -05

Lecture 7: Social network -06

Chapter 22: Subgraphs

Lecture 1: Subgraphs

Lecture 2: Triangles

Chapter 23: Facebook Social Network Analysis

Lecture 1: Facebook Social Network Analysis

Lecture 2: Facebook Social Network Analysis

Chapter 24: Twitter Social Network Analysis

Lecture 1: Creating Twitter Apps Account

Lecture 2: Connecting to Twitter API

Lecture 3: Getting Data using the Twitter API

Lecture 4: Working with retweets data

Lecture 5: NetworkX Layouts

Lecture 6: Making networks with Twitter Data

Lecture 7: Graphing Degree Centrality -03

Lecture 8: Creating DiGraphs with twitter data

Lecture 9: Graphing in and out degree centralities

Chapter 25: Conclusions

Lecture 1: Thank you & Good Bye

Instructors

Theophilus Siameh
Spark Developer ,Kafka and Data Scientist

Rating Distribution

1 stars: 33 votes
2 stars: 43 votes
3 stars: 113 votes
4 stars: 186 votes
5 stars: 236 votes

Frequently Asked Questions

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