Social Network Analysis(SNA) and Graph Analysis using Python

Social Network Analysis(SNA) and Graph Analysis using Python, available at $49.99, has an average rating of 2.45, with 34 lectures, based on 29 reviews, and has 313 subscribers.

You will learn about 1. The content (80% hands on and 20% theory) will prepare you to work independently on SNA projects 2. Learn – Basic, Intermediate and Advance concepts 3. Graph’s foundations (20 techniques) 4. Graph’s use cases (6 use cases) 5. Link Analysis (how Google search the best link/page for you) 6. Page Ranks 7. Hyperlink-Induced Topic Search (HITS; also known as hubs and authorities) 8. Node embedding 9. Recommendations using SNA (theory) 10. Management and monitoring of complex networks (theory) 11. How to use SNA for Data Analytics (theory) This course is ideal for individuals who are Anyone who want to Learn and Apply SNA using Python or Anyone who want to Learn advance Machine Learning It is particularly useful for Anyone who want to Learn and Apply SNA using Python or Anyone who want to Learn advance Machine Learning.

Enroll now: Social Network Analysis(SNA) and Graph Analysis using Python

Summary

Title: Social Network Analysis(SNA) and Graph Analysis using Python

Price: $49.99

Average Rating: 2.45

Number of Lectures: 34

Number of Published Lectures: 34

Number of Curriculum Items: 34

Number of Published Curriculum Objects: 34

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

1. The content (80% hands on and 20% theory) will prepare you to work independently on SNA projects

2. Learn – Basic, Intermediate and Advance concepts

3. Graph’s foundations (20 techniques)

4. Graph’s use cases (6 use cases)

5. Link Analysis (how Google search the best link/page for you)

6. Page Ranks

7. Hyperlink-Induced Topic Search (HITS; also known as hubs and authorities)

8. Node embedding

9. Recommendations using SNA (theory)

10. Management and monitoring of complex networks (theory)

11. How to use SNA for Data Analytics (theory)

Who Should Attend

Anyone who want to Learn and Apply SNA using Python

Anyone who want to Learn advance Machine Learning

Target Audiences

Anyone who want to Learn and Apply SNA using Python

Anyone who want to Learn advance Machine Learning

As practitioner of SNA, I am trying to bring many relevant topics under one umbrella in following topics so that it can be uses in advance machine learning areas.

1. The content (80% hands on and 20% theory) will prepare you to work independently on SNA projects

2. Learn – Basic, Intermediate and Advance concepts

3. Graph’s foundations (20 techniques)

4. Graph’s use cases (6 use cases)

5. Link Analysis (how Google search the best link/page for you)

6. Page Ranks

7. Hyperlink-Induced Topic Search (HITS; also known as hubs and authorities)

8. Node embedding

9. Recommendations using SNA (theory)

10. Management and monitoring of complex networks (theory)

11. How to use SNA for Data Analytics (theory)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Installations , Technology , Folder structure

Chapter 2: Graphs foundations

Lecture 1: History of Graph Theory

Lecture 2: Definitions of Graph

Lecture 3: Graph foundations_Data Preparations

Lecture 4: Graph foundations_Explore undirectional graph

Lecture 5: Small world – Six degrees of separation

Lecture 6: Small world – Six degrees of separation_code

Lecture 7: Diameter – Transitivity – SubTree – Eccentricity – Closeness Centrality-Eigenve

Lecture 8: Betweenness centrality – communities – cliques – Adjacency matrix

Lecture 9: Directional Graph

Lecture 10: sna_basic_seealsology_data

Chapter 3: Use case: Airlines

Lecture 1: Use case airlines data preparations

Lecture 2: Density – Transitivity – Layouts – Diameter – Shortest paths

Lecture 3: Degree – Subtree – Eccentricity – Closeness – Eigenvector – Betweenness – Commun

Lecture 4: Few practical question and answer

Chapter 4: Use case: Fraudulent network data analysis

Lecture 1: Usecase_fraudulent_network_data_preprations

Lecture 2: Transitivity – Closeness – Eigenvector – Betweenness – Communities – Directional

Lecture 3: KMean clustering

Lecture 4: Advance Statistics – Fraud score calculation

Lecture 5: Supervised Analytics

Chapter 5: Use case: Enron scandal

Lecture 1: Enron_introduction and data loading

Lecture 2: Clean and Prepare data for unidirectional graph

Lecture 3: Density – Transitivity – Layouts – nx visualization

Lecture 4: Degree – Subtree – Eccentricity – Closeness – Eigenvector – Betweenness – Commun

Lecture 5: Class work – Please do yourself line by line

Chapter 6: Extended features of Graph

Lecture 1: Page Rank

Lecture 2: Page Rank – code

Chapter 7: Node Embedding

Lecture 1: Node Embeddings – Pre requisites

Lecture 2: Word embedding

Lecture 3: Node Embedding – Definition and Methods

Lecture 4: Node Embedding using Deep Walk

Lecture 5: Node Embedding using Node2Vec

Chapter 8: Miscellaneous

Lecture 1: How to use SNA for Data Analytics

Instructors

Shiv Onkar Deepak Kumar
AI Researcher and Consultant, Chief Data Scientist

Rating Distribution

1 stars: 4 votes

2 stars: 4 votes

3 stars: 4 votes

4 stars: 8 votes

5 stars: 9 votes

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