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.
Este curso faz parte do Programa de cursos integrados Big DataBig Data
Graph Analytics for Big Data
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Informações sobre o curso
31,862 visualizações recentes
Habilidades que você terá
Graph TheoryNeo4jAnalyticsGraph Database
Programa - O que você aprenderá com este curso
Semana
14 minutos para concluir
Welcome to Graph Analytics
Meet your instructor, Amarnath Gupta and learn about the course objectives.
1 vídeo ((Total 4 mín.))
1 vídeos
Semana
23 horas para concluir
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 vídeos ((Total 38 mín.)), 2 leituras, 2 testes
8 videos
What is a Graph?7min
Why Graphs?2min
Why Graphs? Example 1: Social Networking3min
Why Graphs? Example 2: Biological Networks3min
Why Graphs? Example 3: Human Information Network Analytics3min
Why Graphs? Example 4: Smart Cities2min
The Purpose of Analytics1min
What are the impact of Big Data's V's on Graphs?12min
2 leituras
What to learn in this module10min
Download Slides for this Module10min
1 exercício prático
Introduction to Graphs24min
Semana
33 horas para concluir
Graph Analytics
17 vídeos ((Total 81 mín.)), 3 leituras, 2 testes
17 videos
Path Analytics6min
The Basic Path Analytics Question: What is the Best Path?4min
Applying Dijkstra's Algorithm5min
Inclusion and Exclusion Constraints1min
Connectivity Analytics3min
Disconnecting a Graph1min
Connectedness: Indegree and Outdegree3min
Community Analytics and Local Properties7min
Global Property: Modularity7min
Centrality Analytics8min
Optional Lecture 1: Bi-directional Dijkstra Algorithm2min
Optional Lecture 2: Goal-directed Dijkstra Algorithm2min
Optional Lecture 3: Power Law Graphs2min
Optional Lecture 4: Measuring Graph Evolution3min
Optional Lecture 5: Eigenvector Centrality6min
Optional Lecture 6: Key Player Problems2min
3 leituras
What to learn in this module10min
If this module takes a little longer... that's OK!10min
Download All Slides for Module 310min
2 exercícios práticos
Graph Analytics Applications34min
Connectivity, Community, and Centrality Analytics38min
Semana
44 horas para concluir
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 vídeos ((Total 78 mín.)), 12 leituras, 2 testes
10 videos
Hands-On: Downloading, Installing, and Running Neo4j5min
Hands-On: Getting Started With Neo4j6min
Hands-On: Modifying a Graph With Neo4j7min
Hands-On: Importing Data Into Neo4j13min
Hands-On: Basic Queries in Neo4j With Cypher - Part 16min
Hands-On: Basic Queries in Neo4j With Cypher - Part 26min
Hands-On: Path Analytics in Neo4j Using Cypher - Part 18min
Hands-On: Path Analytics in Neo4j Using Cypher - Part 211min
Hands-On: Connectivity Analytics in Neo4j With Cypher9min
12 leituras
About the Supplementary Resources10min
Downloading, Installing, and Running Neo4j - Supplementary Resources10min
Getting Started With Neo4j - Supplementary Resources10min
Adding to and Modifying a Graph - Supplementary Resources10min
Download datasets used in this Graph Analytics with Neo4j10min
Importing Data Into Neo4j - Supplementary Resources10min
FAQ10min
Basic Queries in Neo4j With Cypher - Supplementary Resources10min
Path Analytics in Neo4j With Cypher - Supplementary Resources10min
Connectivity Analytics in Neo4j with Cypher - Supplementary Resources10min
Assignment: Practicing Graph Analytics in Neo4j With Cypher10min
Download All Neo4j Supplementary Resources (PDFs)10min
2 exercícios práticos
Quiz: Graph Analytics With Neo4j18min
Assessment Questions on 'Practicing Graph Analytics in Neo4j With Cypher'18min
50%
comecei uma nova carreira após concluir estes cursos
30%
consegui um benefício significativo de carreira com este curso
Principais avaliações do 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.
Instrutores
Amarnath Gupta
Director, Advanced Query Processing LabSan Diego Supercomputer Center (SDSC)
Sobre Universidade da Califórnia, 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.
Sobre o Programa de cursos integrados Big DataBig 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.
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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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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