Graph Analytics for Big Data

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Graph Analytics for Big Data

This course is part of Big Data Specialization

Instructor: Amarnath Gupta

57,094 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

1,281 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.

1,281 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

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Assessments

6 assignments¹

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Taught in English

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This course is part of the Big Data Specialization

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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 videosTotal 38 minutes

What is a Graph?8 minutes
Why Graphs?3 minutes
Why Graphs? Example 1: Social Networking4 minutes
Why Graphs? Example 2: Biological Networks4 minutes
Why Graphs? Example 3: Human Information Network Analytics4 minutes
Why Graphs? Example 4: Smart Cities3 minutes
The Purpose of Analytics1 minute
What are the impact of Big Data's V's on Graphs?13 minutes

2 readingsTotal 20 minutes

What to learn in this module10 minutes
Download Slides for this Module10 minutes

1 assignmentTotal 30 minutes

Introduction to Graphs30 minutes

1 peer reviewTotal 60 minutes

Graphs in Everyday Life60 minutes

2 discussion promptsTotal 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 videosTotal 81 minutes

Focusing On Graph Analytics Techniques10 minutes
Path Analytics6 minutes
The Basic Path Analytics Question: What is the Best Path?4 minutes
Applying Dijkstra's Algorithm5 minutes
Inclusion and Exclusion Constraints1 minute
Connectivity Analytics4 minutes
Disconnecting a Graph2 minutes
Connectedness: Indegree and Outdegree3 minutes
Community Analytics and Local Properties8 minutes
Global Property: Modularity8 minutes
Centrality Analytics8 minutes
Optional Lecture 1: Bi-directional Dijkstra Algorithm2 minutes
Optional Lecture 2: Goal-directed Dijkstra Algorithm3 minutes
Optional Lecture 3: Power Law Graphs3 minutes
Optional Lecture 4: Measuring Graph Evolution4 minutes
Optional Lecture 5: Eigenvector Centrality7 minutes
Optional Lecture 6: Key Player Problems3 minutes

3 readingsTotal 30 minutes

What to learn in this module10 minutes
If this module takes a little longer... that's OK!10 minutes
Download All Slides for Module 310 minutes

2 assignmentsTotal 60 minutes

Graph Analytics Applications30 minutes
Connectivity, Community, and Centrality Analytics30 minutes

2 discussion promptsTotal 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 videosTotal 43 minutes

Running Neo4j container2 minutes
Hands-On: Getting Started With Neo4j5 minutes
Hands-On: Modifying a Graph With Neo4j6 minutes
Hands-On: Importing Data Into Neo4j6 minutes
Hands-On: Basic Queries in Neo4j8 minutes
Hands-On: Path Analytics in Neo4j Using Cypher10 minutes
Hands-On: Connectivity Analytics in Neo4j With Cypher6 minutes

9 readingsTotal 90 minutes

Downloading and Installing Docker Desktop10 minutes
Running Neo4j container10 minutes
Hands-On: Getting Started With Neo4j 10 minutes
Hands-On: Modifying a Graph With Neo4j10 minutes
Hands-On: Importing Data Into Neo4j10 minutes
Hands-On: Basic Queries in Neo4j10 minutes
Hands-On: Path Analytics in Neo4j With Cypher10 minutes
Hands-On: Connectivity Analytics in Neo4j with Cypher10 minutes
Assignment: Practicing Graph Analytics in Neo4j With Cypher10 minutes

2 assignmentsTotal 45 minutes

Quiz: Graph Analytics With Neo4j15 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 videosTotal 75 minutes

Introduction: Large Scale Graph Processing1 minute
A Parallel Programming Model for Graphs11 minutes
Pregel: The System That Changed Graph Processing7 minutes
Giraph and GraphX10 minutes
Beyond Single Vertex Computation5 minutes
Introduction to GraphX: Hands-On Demonstrations4 minutes
Hands On: Building a Graph9 minutes
Hands On: Building a Degree Histogram5 minutes
Hands On: Plot the Degree Histogram4 minutes
Hands On: Network Connectedness and Clustering Components10 minutes
Hands On: Joining Graph Datasets8 minutes

6 readingsTotal 60 minutes

Datasets and Libraries for Example of Analytics Hands On10 minutes
Hands On: Building a Graph 10 minutes
Hands On: Building a Degree Histogram10 minutes
Hands On: Plot the Degree Histogram10 minutes
Hands On: Network Connectedness and Clustering Components 10 minutes
Hands On: Joining Graph Datasets10 minutes

1 assignmentTotal 30 minutes

Using GraphX30 minutes

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Instructor

Instructor ratings

(72 ratings)

Amarnath Gupta

University of California San Diego

10 Courses488,016 learners

Offered by

University of California San Diego

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Reviewed on Jul 20, 2017

Gives wonderful overview and hands on experience Graph analytic. Course Contents were very crisp and pretty comprehensive for a novice.

Reviewed on Mar 23, 2019

Nice introduction of multiple graph packages, however I missed some more applied cases, covering a real graph problem investigation (particularly how to associate Neo4j with GraphX).

Reviewed on Apr 8, 2019

Some of the hands-on practices fail due to unknown issues but, after reading the blogs somebody finds solutions but nobody from courser or UC help or adv

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