Об этом курсе: 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.
Автор: University of California San Diego
Преподаватели: Amarnath Gupta, Director, Advanced Query Processing Lab
San Diego Supercomputer Center (SDSC)
| Основные сведения | Course 5 of 6 in the Big Data Specialization |
| Выполнение | 4 Weeks, 3-5 hours/week |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
Welcome to Graph Analytics
Meet your instructor, Amarnath Gupta and learn about the course objectives.
1 видео
НЕДЕЛЯ 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 видео, 2 материала для самостоятельного изучения
- Leyendo: What to learn in this module
- Видео: Why Graphs?
- Cuadro de aviso de la discusión: Let's Discuss: What else do you interact with that can be represented as a graph?
- Видео: Why Graphs? Example 1: Social Networking
- Видео: Why Graphs? Example 2: Biological Networks
- Видео: Why Graphs? Example 3: Human Information Network Analytics
- Видео: Why Graphs? Example 4: Smart Cities
- Видео: The Purpose of Analytics
- Видео: What are the impact of Big Data's V's on Graphs?
- Cuadro de aviso de la discusión: Optional: What's the most interesting graph you reviewed?
- Leyendo: Download Slides for this Module
Оцениваемый: Introduction to Graphs
Оцениваемый: Graphs in Everyday Life
НЕДЕЛЯ 3
Graph Analytics
17 видео, 3 материала для самостоятельного изучения
- Leyendo: What to learn in this module
- Leyendo: If this module takes a little longer... that's OK!
- Leyendo: Download All Slides for Module 3
- Видео: Path Analytics
- Видео: The Basic Path Analytics Question: What is the Best Path?
- Видео: Applying Dijkstra's Algorithm
- Видео: Inclusion and Exclusion Constraints
- Cuadro de aviso de la discusión: Let's Discuss: Where do you see path problems in your life?
- Видео: Connectivity Analytics
- Видео: Disconnecting a Graph
- Видео: Connectedness: Indegree and Outdegree
- Видео: Community Analytics and Local Properties
- Cuadro de aviso de la discusión: Let's Discuss: What kind of community analytics question would you like to ask?
- Видео: Global Property: Modularity
- Видео: Centrality Analytics
- Видео: Optional Lecture 1: Bi-directional Dijkstra Algorithm
- Видео: Optional Lecture 2: Goal-directed Dijkstra Algorithm
- Видео: Optional Lecture 3: Power Law Graphs
- Видео: Optional Lecture 4: Measuring Graph Evolution
- Видео: Optional Lecture 5: Eigenvector Centrality
- Видео: Optional Lecture 6: Key Player Problems
Оцениваемый: Graph Analytics Applications
Оцениваемый: Connectivity, Community, and Centrality Analytics
НЕДЕЛЯ 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 видео, 12 материала для самостоятельного изучения
- Leyendo: About the Supplementary Resources
- Leyendo: Downloading, Installing, and Running Neo4j - Supplementary Resources
- Видео: Hands-On: Downloading, Installing, and Running Neo4j
- Leyendo: Getting Started With Neo4j - Supplementary Resources
- Видео: Hands-On: Getting Started With Neo4j
- Leyendo: Adding to and Modifying a Graph - Supplementary Resources
- Видео: Hands-On: Modifying a Graph With Neo4j
- Leyendo: Download datasets used in this Graph Analytics with Neo4j
- Leyendo: Importing Data Into Neo4j - Supplementary Resources
- Видео: Hands-On: Importing Data Into Neo4j
- Leyendo: FAQ
- Leyendo: Basic Queries in Neo4j With Cypher - Supplementary Resources
- Видео: Hands-On: Basic Queries in Neo4j With Cypher - Part 1
- Видео: Hands-On: Basic Queries in Neo4j With Cypher - Part 2
- Leyendo: Path Analytics in Neo4j With Cypher - Supplementary Resources
- Видео: Hands-On: Path Analytics in Neo4j Using Cypher - Part 1
- Видео: Hands-On: Path Analytics in Neo4j Using Cypher - Part 2
- Leyendo: Connectivity Analytics in Neo4j with Cypher - Supplementary Resources
- Видео: Hands-On: Connectivity Analytics in Neo4j With Cypher
- Leyendo: Assignment: Practicing Graph Analytics in Neo4j With Cypher
- Leyendo: Download All Neo4j Supplementary Resources (PDFs)
Оцениваемый: Quiz: Graph Analytics With Neo4j
Оцениваемый: Assessment Questions on 'Practicing Graph Analytics in Neo4j With Cypher'
НЕДЕЛЯ 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 видео, 7 материала для самостоятельного изучения
- Видео: A Parallel Programming Model for Graphs
- Видео: Pregel: The System That Changed Graph Processing
- Видео: Giraph and GraphX
- Видео: Beyond Single Vertex Computation
- Видео: Introduction to GraphX: Hands-On Demonstrations
- Leyendo: Datasets and Libraries for Example of Analytics Hands On
- Leyendo: Download all of the readings for this section as a PDF
- Видео: Hands On: Building a Graph
- Leyendo: Hands On: Building a Graph Reading
- Видео: Hands On: Building a Degree Histogram
- Leyendo: Hands On: Building a Degree Histogram Reading
- Видео: Hands On: Plot the Degree Histogram
- Leyendo: Hands On: Plot the Degree Histogram Reading
- Видео: Hands On: Network Connectedness and Clustering Components
- Leyendo: Hands On: Network Connectedness and Clustering Components Reading
- Видео: Hands On: Joining Graph Datasets
- Leyendo: Hands On: Joining Graph Datasets Reading
Оцениваемый: Using GraphX
Часто задаваемые вопросы
Как это работает
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Авторы
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.
Стоимость
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Рейтинги и отзывы
Thanks
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.
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Nice & complete. intuitive way to learn