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.
Ce cours fait partie de la Spécialisation Big Data
Graph Analytics for Big Data
proposé par
À propos de ce cours
30,809 consultations récentes
Compétences que vous acquerrez
Graph TheoryNeo4jAnalyticsGraph Database
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
14 minutes pour terminer
Welcome to Graph Analytics
Meet your instructor, Amarnath Gupta and learn about the course objectives.
1 vidéo (Total 4 min)
1 vidéo
Semaine
23 heures pour terminer
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 vidéos (Total 38 min), 2 lectures, 2 quiz
8 vidéos
What is a Graph?7 min
Why Graphs?2 min
Why Graphs? Example 1: Social Networking3 min
Why Graphs? Example 2: Biological Networks3 min
Why Graphs? Example 3: Human Information Network Analytics3 min
Why Graphs? Example 4: Smart Cities2 min
The Purpose of Analytics1 min
What are the impact of Big Data's V's on Graphs?12 min
2 lectures
What to learn in this module10 min
Download Slides for this Module10 min
1 exercice pour s'entraîner
Introduction to Graphs24 min
Semaine
33 heures pour terminer
Graph Analytics
17 vidéos (Total 81 min), 3 lectures, 2 quiz
17 vidéos
Path Analytics6 min
The Basic Path Analytics Question: What is the Best Path?4 min
Applying Dijkstra's Algorithm5 min
Inclusion and Exclusion Constraints1 min
Connectivity Analytics3 min
Disconnecting a Graph1 min
Connectedness: Indegree and Outdegree3 min
Community Analytics and Local Properties7 min
Global Property: Modularity7 min
Centrality Analytics8 min
Optional Lecture 1: Bi-directional Dijkstra Algorithm2 min
Optional Lecture 2: Goal-directed Dijkstra Algorithm2 min
Optional Lecture 3: Power Law Graphs2 min
Optional Lecture 4: Measuring Graph Evolution3 min
Optional Lecture 5: Eigenvector Centrality6 min
Optional Lecture 6: Key Player Problems2 min
3 lectures
What to learn in this module10 min
If this module takes a little longer... that's OK!10 min
Download All Slides for Module 310 min
2 exercices pour s'entraîner
Graph Analytics Applications34 min
Connectivity, Community, and Centrality Analytics38 min
Semaine
44 heures pour terminer
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 vidéos (Total 78 min), 12 lectures, 2 quiz
10 vidéos
Hands-On: Downloading, Installing, and Running Neo4j5 min
Hands-On: Getting Started With Neo4j6 min
Hands-On: Modifying a Graph With Neo4j7 min
Hands-On: Importing Data Into Neo4j13 min
Hands-On: Basic Queries in Neo4j With Cypher - Part 16 min
Hands-On: Basic Queries in Neo4j With Cypher - Part 26 min
Hands-On: Path Analytics in Neo4j Using Cypher - Part 18 min
Hands-On: Path Analytics in Neo4j Using Cypher - Part 211 min
Hands-On: Connectivity Analytics in Neo4j With Cypher9 min
12 lectures
About the Supplementary Resources10 min
Downloading, Installing, and Running Neo4j - Supplementary Resources10 min
Getting Started With Neo4j - Supplementary Resources10 min
Adding to and Modifying a Graph - Supplementary Resources10 min
Download datasets used in this Graph Analytics with Neo4j10 min
Importing Data Into Neo4j - Supplementary Resources10 min
FAQ10 min
Basic Queries in Neo4j With Cypher - Supplementary Resources10 min
Path Analytics in Neo4j With Cypher - Supplementary Resources10 min
Connectivity Analytics in Neo4j with Cypher - Supplementary Resources10 min
Assignment: Practicing Graph Analytics in Neo4j With Cypher10 min
Download All Neo4j Supplementary Resources (PDFs)10 min
2 exercices pour s'entraîner
Quiz: Graph Analytics With Neo4j18 min
Assessment Questions on 'Practicing Graph Analytics in Neo4j With Cypher'18 min
4.2
152 avis50%
a commencé une nouvelle carrière après avoir terminé ces cours
30%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Principaux examens pour 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.
Enseignant
Amarnath Gupta
Director, Advanced Query Processing LabSan Diego Supercomputer Center (SDSC)
À propos de Université de Californie à 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.
À propos de la Spécialisation Big Data
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.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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