Informações sobre o curso: 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.
Desenvolvido por: University of California San Diego
Ministrado por: Amarnath Gupta, Director, Advanced Query Processing Lab
San Diego Supercomputer Center (SDSC)
| Informações básicas | Curso 5 de 6 no Big Data Specialization |
| Compromisso | 4 Weeks, 3-5 hours/week |
| Idioma | English |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
Programa
SEMANA 1
Welcome to Graph Analytics
Meet your instructor, Amarnath Gupta and learn about the course objectives.
1 vídeos
SEMANA 2
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, 2 leituras
- Reading: What to learn in this module
- Vídeo: Why Graphs?
- Discussion Prompt: Let's Discuss: What else do you interact with that can be represented as a graph?
- Vídeo: Why Graphs? Example 1: Social Networking
- Vídeo: Why Graphs? Example 2: Biological Networks
- Vídeo: Why Graphs? Example 3: Human Information Network Analytics
- Vídeo: Why Graphs? Example 4: Smart Cities
- Vídeo: The Purpose of Analytics
- Vídeo: What are the impact of Big Data's V's on Graphs?
- Discussion Prompt: Optional: What's the most interesting graph you reviewed?
- Reading: Download Slides for this Module
Nota atribuída: Introduction to Graphs
Nota atribuída: Graphs in Everyday Life
SEMANA 3
Graph Analytics
17 videos, 3 leituras
- Reading: What to learn in this module
- Reading: If this module takes a little longer... that's OK!
- Reading: Download All Slides for Module 3
- Vídeo: Path Analytics
- Vídeo: The Basic Path Analytics Question: What is the Best Path?
- Vídeo: Applying Dijkstra's Algorithm
- Vídeo: Inclusion and Exclusion Constraints
- Discussion Prompt: Let's Discuss: Where do you see path problems in your life?
- Vídeo: Connectivity Analytics
- Vídeo: Disconnecting a Graph
- Vídeo: Connectedness: Indegree and Outdegree
- Vídeo: Community Analytics and Local Properties
- Discussion Prompt: Let's Discuss: What kind of community analytics question would you like to ask?
- Vídeo: Global Property: Modularity
- Vídeo: Centrality Analytics
- Vídeo: Optional Lecture 1: Bi-directional Dijkstra Algorithm
- Vídeo: Optional Lecture 2: Goal-directed Dijkstra Algorithm
- Vídeo: Optional Lecture 3: Power Law Graphs
- Vídeo: Optional Lecture 4: Measuring Graph Evolution
- Vídeo: Optional Lecture 5: Eigenvector Centrality
- Vídeo: Optional Lecture 6: Key Player Problems
Nota atribuída: Graph Analytics Applications
Nota atribuída: Connectivity, Community, and Centrality Analytics
SEMANA 4
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, 12 leituras
- Reading: About the Supplementary Resources
- Reading: Downloading, Installing, and Running Neo4j - Supplementary Resources
- Vídeo: Hands-On: Downloading, Installing, and Running Neo4j
- Reading: Getting Started With Neo4j - Supplementary Resources
- Vídeo: Hands-On: Getting Started With Neo4j
- Reading: Adding to and Modifying a Graph - Supplementary Resources
- Vídeo: Hands-On: Modifying a Graph With Neo4j
- Reading: Download datasets used in this Graph Analytics with Neo4j
- Reading: Importing Data Into Neo4j - Supplementary Resources
- Vídeo: Hands-On: Importing Data Into Neo4j
- Reading: FAQ
- Reading: Basic Queries in Neo4j With Cypher - Supplementary Resources
- Vídeo: Hands-On: Basic Queries in Neo4j With Cypher - Part 1
- Vídeo: Hands-On: Basic Queries in Neo4j With Cypher - Part 2
- Reading: Path Analytics in Neo4j With Cypher - Supplementary Resources
- Vídeo: Hands-On: Path Analytics in Neo4j Using Cypher - Part 1
- Vídeo: Hands-On: Path Analytics in Neo4j Using Cypher - Part 2
- Reading: Connectivity Analytics in Neo4j with Cypher - Supplementary Resources
- Vídeo: Hands-On: Connectivity Analytics in Neo4j With Cypher
- Reading: Assignment: Practicing Graph Analytics in Neo4j With Cypher
- Reading: Download All Neo4j Supplementary Resources (PDFs)
Nota atribuída: Quiz: Graph Analytics With Neo4j
Nota atribuída: Assessment Questions on 'Practicing Graph Analytics in Neo4j With Cypher'
SEMANA 5
Computing Platforms for Graph Analytics
In the last two modules we have learned about graph analytics and graph data management. This week we will study how they come together. There are programming models and software frameworks created specifically for graph analytics. In this module we'll give an introductory tour of these models and frameworks. We will learn to implement what you learned in Week 2 and build on it using GraphX and Giraph.
11 videos, 7 leituras
- Vídeo: A Parallel Programming Model for Graphs
- Vídeo: Pregel: The System That Changed Graph Processing
- Vídeo: Giraph and GraphX
- Vídeo: Beyond Single Vertex Computation
- Vídeo: Introduction to GraphX: Hands-On Demonstrations
- Reading: Datasets and Libraries for Example of Analytics Hands On
- Reading: Download all of the readings for this section as a PDF
- Vídeo: Hands On: Building a Graph
- Reading: Hands On: Building a Graph Reading
- Vídeo: Hands On: Building a Degree Histogram
- Reading: Hands On: Building a Degree Histogram Reading
- Vídeo: Hands On: Plot the Degree Histogram
- Reading: Hands On: Plot the Degree Histogram Reading
- Vídeo: Hands On: Network Connectedness and Clustering Components
- Reading: Hands On: Network Connectedness and Clustering Components Reading
- Vídeo: Hands On: Joining Graph Datasets
- Reading: Hands On: Joining Graph Datasets Reading
Nota atribuída: Using GraphX
Perguntas frequentes
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Desenvolvedores
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.
Custo
| Comprar curso | |
|---|---|
| Acesso aos materiais do curso | Disponível |
| Acesso a materiais valendo nota | Disponível |
| Receba uma nota final | Disponível |
| Obtenha um Certificado de Curso compartilhável | Disponível |
Classificações e avaliações
Good course.
This is the more challenging course of the Specialization. The course is not as well built as the other ones. Week 1 does not include any lessons; week 5 is very fastidious and time consuming. The course should mention up front to run the Cloudera VM with one CPU only to avoid getting wrong metro vertex results and crash the system when using the filter method. The author may want to consider using Python instead of Scala as Python was used in all the previous courses of the specialization.
Useful.
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