|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/big-data-project
课程评论：没有评论
课程名称：大数据 - 学位项目总结 课程概述：欢迎参加大数据学位项目的总结项目！在这个最终项目中，您将利用之前课程中的工具和方法构建一个大数据生态系统。您将分析一个模拟的数据集，该数据集模拟了大量用户在玩我们虚构游戏“抓粉红火烈鸟”时产生的大数据。在为期五周的总结项目中，您将经历大数据科学的典型步骤，包括获取、探索、准备、分析和报告数据。在前两周，我们将介绍数据集，并指导您使用Splunk和Open Office等工具进行一些探索性分析。随后，我们将进入更具挑战性的“大数据”问题，使用您学到的更高级工具，包括KNIME、Spark的MLLib和Gephi。最后，在第五周，我们将教您如何整合所有内容，创建引人入胜且有说服力的报告和幻灯片演示。由于我们与专注于分析机器生成大数据的软件公司Splunk的合作，表现优秀的项目Learners将有机会向Splunk展示并与Splunk的招聘人员和工程领导会面。 课程大纲： 1. 数据的获取、探索和准备 描述：我们将开始处理模拟的游戏数据，探索并准备数据以供大数据分析应用程序使用。 2. 使用KNIME进行数据分类 描述：本周我们将使用KNIME进行数据分类。 3. 使用Spark进行聚类分析 描述：本周我们将使用Spark进行聚类分析。 4. 使用Neo4j对模拟聊天数据进行图形分析 描述：本周我们运用“图形分析与大数据”课程所学知识，对来自“抓粉红火烈鸟”的模拟聊天数据使用Neo4j进行分析，探讨玩家聊天行为并寻找改善游戏的途径。 5. 报告和展示您的工作 描述： 6. 最终提交 描述：
Part: 1
Title:Acquiring, Exploring, and Preparing the Data
Description:Next, we begin working with the simulated game data by exploring and preparing the data for ingestion into big data analytics applications.
Part: 2
Title:Data Classification with KNIME
Description:This week we do some data classification using KNIME.
Part: 3
Title:Clustering with Spark
Description:This week we do some clustering with Spark.
Part: 4
Title:Graph Analytics of Simulated Chat Data With Neo4j
Description:This week we apply what we learned from the 'Graph Analytics With Big Data' course to simulated chat data from Catch the Pink Flamingos using Neo4j. We analyze player chat behavior to find ways of improving the game.
Part: 5
Title:Reporting and Presenting Your Work
Description:
Part: 6
Title:Final Submission
Description:
Welcome to the Capstone Project for Big Data! In this culminating project, you will build a big data ecosystem using tools and methods form the earlier courses in this specialization. You will analyze a data set simulating big data generated from a large number of users who are playing our imaginary game "Catch the Pink Flamingo". During the five week Capstone Project, you will walk through the typical big data science steps for acquiring, exploring, preparing, analyzing, and reporting. In the first two weeks, we will introduce you to the data set and guide you through some exploratory analysis using tools such as Splunk and Open Office. Then we will move into more challenging big data problems requiring the more advanced tools you have learned including KNIME, Spark's MLLib and Gephi. Finally, during the fifth and final week, we will show you how to bring it all together to create engaging and compelling reports and slide presentations. As a result of our collaboration with Splunk, a software company focus on analyzing machine-generated big data, learners with the top projects will be eligible to present to Splunk and meet Splunk recruiters and engineering leadership.
