Graph Analytics for Big Data

Graph Analytics for Big Data

This course is part of Big Data Specialization

Instructor: Amarnath Gupta

Access provided by University of Warwick

56,554 already enrolled

5 modules

Gain insight into a topic and learn the fundamentals.

1,279 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

90%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

1,279 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

90%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Query Languages

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Assessments

6 assignments¹

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Taught in English

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There are 5 modules in this course

Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data.

Meet your instructor, Amarnath Gupta and learn about the course objectives.

What's included

1 video1 reading

Welcome! This week we will get a first exposure to graphs and their use in everyday life. By the end of the module you will be able to create a graph applying core mathematical properties of graphs, and identify the kinds of analysis questions one might be able to ask of such a graph. We hope the you will be inspired as to how graphical representations might enable you to answer new Big Data problems!

What's included

8 videos2 readings1 assignment1 peer review2 discussion prompts

8 videos Total 38 minutes

What is a Graph? 8 minutes
Why Graphs? 3 minutes
Why Graphs? Example 1: Social Networking 4 minutes
Why Graphs? Example 2: Biological Networks 4 minutes
Why Graphs? Example 3: Human Information Network Analytics 4 minutes
Why Graphs? Example 4: Smart Cities 3 minutes
The Purpose of Analytics 1 minute
What are the impact of Big Data's V's on Graphs? 13 minutes

2 readings Total 20 minutes

What to learn in this module 10 minutes
Download Slides for this Module 10 minutes

1 assignment Total 30 minutes

Introduction to Graphs 30 minutes

1 peer review Total 60 minutes

Graphs in Everyday Life 60 minutes

2 discussion prompts Total 20 minutes

Let's Discuss: What else do you interact with that can be represented as a graph? 10 minutes
Optional: What's the most interesting graph you reviewed? 10 minutes

What's included

17 videos3 readings2 assignments2 discussion prompts

17 videos Total 81 minutes

Focusing On Graph Analytics Techniques 10 minutes
Path Analytics 6 minutes
The Basic Path Analytics Question: What is the Best Path? 4 minutes
Applying Dijkstra's Algorithm 5 minutes
Inclusion and Exclusion Constraints 1 minute
Connectivity Analytics 4 minutes
Disconnecting a Graph 2 minutes
Connectedness: Indegree and Outdegree 3 minutes
Community Analytics and Local Properties 8 minutes
Global Property: Modularity 8 minutes
Centrality Analytics 8 minutes
Optional Lecture 1: Bi-directional Dijkstra Algorithm 2 minutes
Optional Lecture 2: Goal-directed Dijkstra Algorithm 3 minutes
Optional Lecture 3: Power Law Graphs 3 minutes
Optional Lecture 4: Measuring Graph Evolution 4 minutes
Optional Lecture 5: Eigenvector Centrality 7 minutes
Optional Lecture 6: Key Player Problems 3 minutes

3 readings Total 30 minutes

What to learn in this module 10 minutes
If this module takes a little longer... that's OK! 10 minutes
Download All Slides for Module 3 10 minutes

2 assignments Total 60 minutes

Graph Analytics Applications 30 minutes
Connectivity, Community, and Centrality Analytics 30 minutes

2 discussion prompts Total 20 minutes

Let's Discuss: Where do you see path problems in your life? 10 minutes
Let's Discuss: What kind of community analytics question would you like to ask? 10 minutes

Welcome to the 4th module in the Graph Analytics course. Last week, we got a glimpse of a number of graph properties and why they are important. This week we will use those properties for analyzing graphs using a free and powerful graph analytics tool called Neo4j. We will demonstrate how to use Cypher, the query language of Neo4j, to perform a wide range of analyses on a variety of graph networks.

What's included

7 videos9 readings2 assignments

7 videos Total 43 minutes

Running Neo4j container 2 minutes
Hands-On: Getting Started With Neo4j 5 minutes
Hands-On: Modifying a Graph With Neo4j 6 minutes
Hands-On: Importing Data Into Neo4j 6 minutes
Hands-On: Basic Queries in Neo4j 8 minutes
Hands-On: Path Analytics in Neo4j Using Cypher 10 minutes
Hands-On: Connectivity Analytics in Neo4j With Cypher 6 minutes

9 readings Total 90 minutes

Downloading and Installing Docker Desktop 10 minutes
Running Neo4j container 10 minutes
Hands-On: Getting Started With Neo4j 10 minutes
Hands-On: Modifying a Graph With Neo4j 10 minutes
Hands-On: Importing Data Into Neo4j 10 minutes
Hands-On: Basic Queries in Neo4j 10 minutes
Hands-On: Path Analytics in Neo4j With Cypher 10 minutes
Hands-On: Connectivity Analytics in Neo4j with Cypher 10 minutes
Assignment: Practicing Graph Analytics in Neo4j With Cypher 10 minutes

2 assignments Total 45 minutes

Quiz: Graph Analytics With Neo4j 15 minutes
Assessment Questions on 'Practicing Graph Analytics in Neo4j With Cypher' 30 minutes

In the last two modules we have learned about graph analytics and graph data management. This week we will study how they come together. There are programming models and software frameworks created specifically for graph analytics. In this module we'll give an introductory tour of these models and frameworks. We will learn to implement what you learned in Week 2 and build on it using GraphX and Giraph.

What's included

11 videos6 readings1 assignment

11 videos Total 75 minutes

Introduction: Large Scale Graph Processing 1 minute
A Parallel Programming Model for Graphs 11 minutes
Pregel: The System That Changed Graph Processing 7 minutes
Giraph and GraphX 10 minutes
Beyond Single Vertex Computation 5 minutes
Introduction to GraphX: Hands-On Demonstrations 4 minutes
Hands On: Building a Graph 9 minutes
Hands On: Building a Degree Histogram 5 minutes
Hands On: Plot the Degree Histogram 4 minutes
Hands On: Network Connectedness and Clustering Components 10 minutes
Hands On: Joining Graph Datasets 8 minutes

6 readings Total 60 minutes

Datasets and Libraries for Example of Analytics Hands On 10 minutes
Hands On: Building a Graph 10 minutes
Hands On: Building a Degree Histogram 10 minutes
Hands On: Plot the Degree Histogram 10 minutes
Hands On: Network Connectedness and Clustering Components 10 minutes
Hands On: Joining Graph Datasets 10 minutes

1 assignment Total 30 minutes

Using GraphX 30 minutes

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Instructor

Instructor ratings

(72 ratings)

Amarnath Gupta

University of California San Diego

10 Courses 484,565 learners

Offered by

University of California San Diego

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Reviewed on Jul 16, 2017

The Course was amazing. It was very much helpful in getting the insights and developed my skills in analyzing the data using Graphs. Thank You Coursera and UC, San Diego Team.

Reviewed on Oct 27, 2018

I found a new love in this course Neo4j. Graphs are really powerful. You should expect a very intensive theoretical and hands-on knowledge to takeaway from this course. Think like a vertex ....

Reviewed on Apr 2, 2020

Generally a good course. However, the last week was just a "look and follow" week. The only thing I take from this week is that things are possible with Spark, but that's it.

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