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.
提供：
シラバス - 本コースの学習内容
週
14分で修了
Welcome to Graph Analytics
Meet your instructor, Amarnath Gupta and learn about the course objectives.
1件のビデオ （合計4分）
週
23時間で修了
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件のビデオ （合計38分）, 2 readings, 2 quizzes
8件のビデオ
Why Graphs?2 分
Why Graphs? Example 1: Social Networking3 分
Why Graphs? Example 2: Biological Networks3 分
Why Graphs? Example 3: Human Information Network Analytics3 分
Why Graphs? Example 4: Smart Cities2 分
The Purpose of Analytics1 分
What are the impact of Big Data's V's on Graphs?12 分
2件の学習用教材
What to learn in this module10 分
Download Slides for this Module10 分
1の練習問題
Introduction to Graphs24 分
週
33時間で修了
Graph Analytics
17件のビデオ （合計81分）, 3 readings, 2 quizzes
17件のビデオ
Path Analytics6 分
The Basic Path Analytics Question: What is the Best Path?4 分
Applying Dijkstra's Algorithm5 分
Inclusion and Exclusion Constraints1 分
Connectivity Analytics3 分
Disconnecting a Graph1 分
Connectedness: Indegree and Outdegree3 分
Community Analytics and Local Properties7 分
Global Property: Modularity7 分
Centrality Analytics8 分
Optional Lecture 1: Bi-directional Dijkstra Algorithm2 分
Optional Lecture 2: Goal-directed Dijkstra Algorithm2 分
Optional Lecture 3: Power Law Graphs2 分
Optional Lecture 4: Measuring Graph Evolution3 分
Optional Lecture 5: Eigenvector Centrality6 分
Optional Lecture 6: Key Player Problems2 分
3件の学習用教材
What to learn in this module10 分
If this module takes a little longer... that's OK!10 分
Download All Slides for Module 310 分
2の練習問題
Graph Analytics Applications34 分
Connectivity, Community, and Centrality Analytics38 分
週
44時間で修了
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件のビデオ （合計78分）, 12 readings, 2 quizzes
10件のビデオ
Hands-On: Downloading, Installing, and Running Neo4j5 分
Hands-On: Getting Started With Neo4j6 分
Hands-On: Modifying a Graph With Neo4j7 分
Hands-On: Importing Data Into Neo4j13 分
Hands-On: Basic Queries in Neo4j With Cypher - Part 16 分
Hands-On: Basic Queries in Neo4j With Cypher - Part 26 分
Hands-On: Path Analytics in Neo4j Using Cypher - Part 18 分
Hands-On: Path Analytics in Neo4j Using Cypher - Part 211 分
Hands-On: Connectivity Analytics in Neo4j With Cypher9 分
12件の学習用教材
About the Supplementary Resources10 分
Downloading, Installing, and Running Neo4j - Supplementary Resources10 分
Getting Started With Neo4j - Supplementary Resources10 分
Adding to and Modifying a Graph - Supplementary Resources10 分
Download datasets used in this Graph Analytics with Neo4j10 分
Importing Data Into Neo4j - Supplementary Resources10 分
FAQ10 分
Basic Queries in Neo4j With Cypher - Supplementary Resources10 分
Path Analytics in Neo4j With Cypher - Supplementary Resources10 分
Connectivity Analytics in Neo4j with Cypher - Supplementary Resources10 分
Assignment: Practicing Graph Analytics in Neo4j With Cypher10 分
Download All Neo4j Supplementary Resources (PDFs)10 分
2の練習問題
Quiz: Graph Analytics With Neo4j18 分
Assessment Questions on 'Practicing Graph Analytics in Neo4j With Cypher'18 分
4.2
152件のレビュー50%
コース終了後に新しいキャリアをスタートした
30%
コースが具体的なキャリアアップにつながった
Graph Analytics for Big Data からの人気レビュー
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.
講師
Amarnath Gupta
Director, Advanced Query Processing LabSan Diego Supercomputer Center (SDSC)
カリフォルニア大学サンディエゴ校について
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.
ビッグデータの専門講座について
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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