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. After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.
This course is part of the Big Data Specialization
Graph Analytics for Big Data
Offered By
About this Course
44,313 recent views
Skills you will gain
Graph TheoryNeo4jAnalyticsGraph Database
Learners taking this Course are
- Data Scientists
- Biostatisticians
- Data Engineers
- Machine Learning Engineers
- Data Analysts
Syllabus - What you will learn from this course
Week
14 minutes to complete
Welcome to Graph Analytics
Meet your instructor, Amarnath Gupta and learn about the course objectives.
1 video (Total 4 min)
Week
23 hours to complete
Introduction to Graphs
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!
8 videos (Total 38 min), 2 readings, 2 quizzes
8 videos
Why Graphs?2m
Why Graphs? Example 1: Social Networking3m
Why Graphs? Example 2: Biological Networks3m
Why Graphs? Example 3: Human Information Network Analytics3m
Why Graphs? Example 4: Smart Cities2m
The Purpose of Analytics1m
What are the impact of Big Data's V's on Graphs?12m
2 readings
What to learn in this module10m
Download Slides for this Module10m
1 practice exercise
Introduction to Graphs24m
Week
33 hours to complete
Graph Analytics
17 videos (Total 81 min), 3 readings, 2 quizzes
17 videos
Path Analytics6m
The Basic Path Analytics Question: What is the Best Path?4m
Applying Dijkstra's Algorithm5m
Inclusion and Exclusion Constraints1m
Connectivity Analytics3m
Disconnecting a Graph1m
Connectedness: Indegree and Outdegree3m
Community Analytics and Local Properties7m
Global Property: Modularity7m
Centrality Analytics8m
Optional Lecture 1: Bi-directional Dijkstra Algorithm2m
Optional Lecture 2: Goal-directed Dijkstra Algorithm2m
Optional Lecture 3: Power Law Graphs2m
Optional Lecture 4: Measuring Graph Evolution3m
Optional Lecture 5: Eigenvector Centrality6m
Optional Lecture 6: Key Player Problems2m
3 readings
What to learn in this module10m
If this module takes a little longer... that's OK!10m
Download All Slides for Module 310m
2 practice exercises
Graph Analytics Applications34m
Connectivity, Community, and Centrality Analytics38m
Week
44 hours to complete
Graph Analytics Techniques
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.
10 videos (Total 78 min), 12 readings, 2 quizzes
10 videos
Hands-On: Downloading, Installing, and Running Neo4j5m
Hands-On: Getting Started With Neo4j6m
Hands-On: Modifying a Graph With Neo4j7m
Hands-On: Importing Data Into Neo4j13m
Hands-On: Basic Queries in Neo4j With Cypher - Part 16m
Hands-On: Basic Queries in Neo4j With Cypher - Part 26m
Hands-On: Path Analytics in Neo4j Using Cypher - Part 18m
Hands-On: Path Analytics in Neo4j Using Cypher - Part 211m
Hands-On: Connectivity Analytics in Neo4j With Cypher9m
12 readings
About the Supplementary Resources10m
Downloading, Installing, and Running Neo4j - Supplementary Resources10m
Getting Started With Neo4j - Supplementary Resources10m
Adding to and Modifying a Graph - Supplementary Resources10m
Download datasets used in this Graph Analytics with Neo4j10m
Importing Data Into Neo4j - Supplementary Resources10m
FAQ10m
Basic Queries in Neo4j With Cypher - Supplementary Resources10m
Path Analytics in Neo4j With Cypher - Supplementary Resources10m
Connectivity Analytics in Neo4j with Cypher - Supplementary Resources10m
Assignment: Practicing Graph Analytics in Neo4j With Cypher10m
Download All Neo4j Supplementary Resources (PDFs)10m
2 practice exercises
Quiz: Graph Analytics With Neo4j18m
Assessment Questions on 'Practicing Graph Analytics in Neo4j With Cypher'18m
4.2
158 Reviews45%
started a new career after completing these courses
29%
got a tangible career benefit from this course
Top reviews from Graph Analytics for Big Data
Got an amazing introduction to Graph Analytics in Big Data. Technical issues with Neo4J made this course a little more challenging than necessary. But the introduction to Spark GraphX was invaluable.
This course was excellent as an introduction to Graph Analytics and using Neo4j. Not only did I learn a lot, I've been given tasks related to what I've learned in this course after finishing it.
Instructor
Amarnath Gupta
Director, Advanced Query Processing LabSan Diego Supercomputer Center (SDSC)
About University of California San Diego
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
About the Big Data Specialization
Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.
*********
Do you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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