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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/big-data-graph-analytics
课程评论：没有评论
课程名称：大数据图分析 课程概述： 您是否想了解数据网络结构及其在不同条件下的变化？是否希望掌握如何在图中识别紧密相互作用的聚类？您是否听说过快速发展的图分析领域，并希望进一步学习？本课程提供图分析领域的广泛概述，让您学习建模、存储、检索和分析图形结构数据的新方法。完成此课程后，您将能够将问题建模为图数据库，并以可扩展的方式在图上执行分析任务，更重要的是，您将能够将这些技术应用于理解数据集在您项目中的重要性。 课程大纲： 1. 欢迎来到图分析： - 介绍讲师Amarnath Gupta及课程目标。 2. 图的介绍： - 本周我们将首次接触图及其在日常生活中的应用。通过本模块，您将能够创建一个应用核心数学特性的图，并识别可以对该图提出的分析问题。我们希望能够激励您通过图形表示方式解决新的大数据问题！ 3. 图分析： - （具体内容未列出） 4. 图分析技术： - 欢迎进入图分析课程的第四模块。上一周，我们了解了多种图的属性及其重要性。本周，我们将利用这些属性，通过免费且强大的图分析工具Neo4j进行图的分析。我们将演示如何使用Neo4j的查询语言Cypher，在各种图网络上执行广泛的分析。 5. 图分析的计算平台： - 在前两个模块中，我们学习了图分析和图数据管理。本周我们将研究它们是如何结合在一起的。图分析专门设计了编程模型和软件框架。本模块将简要介绍这些模型和框架，以及如何利用您在第二周学到的知识，使用GraphX和Giraph进行构建。 通过本课程，您将深入了解图分析的核心概念和实用技术，其应用范围广泛，有助于解决各种大数据挑战。
Name:Welcome to Graph Analytics
Description:Meet your instructor, Amarnath Gupta and learn about the course objectives.
Name:Introduction to Graphs
Description: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!
Name:Graph Analytics
Description:
Name:Graph Analytics Techniques
Description: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.
Name:Computing Platforms for Graph Analytics
Description: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.
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.
