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.
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 4 分钟
Welcome to Graph Analytics
Meet your instructor, Amarnath Gupta and learn about the course objectives.
1 个视频 （总计 4 分钟）
周
2完成时间为 3 小时
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 个视频 （总计 38 分钟）, 2 个阅读材料, 2 个测验
8 个视频
Why Graphs?2分钟
Why Graphs? Example 1: Social Networking3分钟
Why Graphs? Example 2: Biological Networks3分钟
Why Graphs? Example 3: Human Information Network Analytics3分钟
Why Graphs? Example 4: Smart Cities2分钟
The Purpose of Analytics1分钟
What are the impact of Big Data's V's on Graphs?12分钟
2 个阅读材料
What to learn in this module10分钟
Download Slides for this Module10分钟
1 个练习
Introduction to Graphs24分钟
周
3完成时间为 3 小时
Graph Analytics
17 个视频 （总计 81 分钟）, 3 个阅读材料, 2 个测验
17 个视频
Path Analytics6分钟
The Basic Path Analytics Question: What is the Best Path?4分钟
Applying Dijkstra's Algorithm5分钟
Inclusion and Exclusion Constraints1分钟
Connectivity Analytics3分钟
Disconnecting a Graph1分钟
Connectedness: Indegree and Outdegree3分钟
Community Analytics and Local Properties7分钟
Global Property: Modularity7分钟
Centrality Analytics8分钟
Optional Lecture 1: Bi-directional Dijkstra Algorithm2分钟
Optional Lecture 2: Goal-directed Dijkstra Algorithm2分钟
Optional Lecture 3: Power Law Graphs2分钟
Optional Lecture 4: Measuring Graph Evolution3分钟
Optional Lecture 5: Eigenvector Centrality6分钟
Optional Lecture 6: Key Player Problems2分钟
3 个阅读材料
What to learn in this module10分钟
If this module takes a little longer... that's OK!10分钟
Download All Slides for Module 310分钟
2 个练习
Graph Analytics Applications34分钟
Connectivity, Community, and Centrality Analytics38分钟
周
4完成时间为 4 小时
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 个视频 （总计 78 分钟）, 12 个阅读材料, 2 个测验
10 个视频
Hands-On: Downloading, Installing, and Running Neo4j5分钟
Hands-On: Getting Started With Neo4j6分钟
Hands-On: Modifying a Graph With Neo4j7分钟
Hands-On: Importing Data Into Neo4j13分钟
Hands-On: Basic Queries in Neo4j With Cypher - Part 16分钟
Hands-On: Basic Queries in Neo4j With Cypher - Part 26分钟
Hands-On: Path Analytics in Neo4j Using Cypher - Part 18分钟
Hands-On: Path Analytics in Neo4j Using Cypher - Part 211分钟
Hands-On: Connectivity Analytics in Neo4j With Cypher9分钟
12 个阅读材料
About the Supplementary Resources10分钟
Downloading, Installing, and Running Neo4j - Supplementary Resources10分钟
Getting Started With Neo4j - Supplementary Resources10分钟
Adding to and Modifying a Graph - Supplementary Resources10分钟
Download datasets used in this Graph Analytics with Neo4j10分钟
Importing Data Into Neo4j - Supplementary Resources10分钟
FAQ10分钟
Basic Queries in Neo4j With Cypher - Supplementary Resources10分钟
Path Analytics in Neo4j With Cypher - Supplementary Resources10分钟
Connectivity Analytics in Neo4j with Cypher - Supplementary Resources10分钟
Assignment: Practicing Graph Analytics in Neo4j With Cypher10分钟
Download All Neo4j Supplementary Resources (PDFs)10分钟
2 个练习
Quiz: Graph Analytics With Neo4j18分钟
Assessment Questions on 'Practicing Graph Analytics in Neo4j With Cypher'18分钟
4.2
152 个审阅50%
完成这些课程后已开始新的职业生涯
30%
通过此课程获得实实在在的工作福利
来自Graph Analytics for Big Data的热门评论
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.
讲师
Amarnath Gupta
Director, Advanced Query Processing LabSan Diego Supercomputer Center (SDSC)
关于 加州大学圣地亚哥分校
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.
关于 大数据 专项课程
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.
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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.
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