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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/big-data-introduction
课程评论：没有评论
课程名称：大数据导论 课程概述：本课程旨在帮助对大数据领域感兴趣的初学者了解大数据时代的背景及其重要性。课程内容针对那些希望熟悉大数据相关术语、核心概念以及如何将大数据应用于商业或职业发展的学员。课程将介绍常见的大数据分析框架Hadoop，使学员能够更容易地进行大数据分析，从而挖掘数据改变世界的潜力。 课程结束时，您将能够： - 描述大数据的整体格局，并举例说明真实世界中的大数据问题，包括人、组织和传感器这三大数据来源。 - 解释大数据的多个维度（如：数据量、数据速度、数据多样性、数据真实性、数据关联度和数据价值）及其对数据采集、监测、存储、分析和报告的影响。 - 通过5个步骤的过程来获得大数据的价值，从而结构化分析。 - 确定什么是大数据问题，什么不是，并能将大数据问题重新表述为数据科学问题。 - 解释用于可扩展大数据分析的架构组件和编程模型。 - 总结Hadoop核心组件的特性和价值，包括YARN资源和作业管理系统、HDFS文件系统以及MapReduce编程模型。 - 安装并运行一个使用Hadoop的程序！ 此课程适合对数据科学感兴趣的初学者，不需要具备编程经验，但需要能够安装应用程序并使用虚拟机来完成实践作业。 硬件要求： - 四核处理器（建议支持VT-x或AMD-V），64位 - 8GB RAM - 20GB可用磁盘空间 软件要求： - 本课程使用几个开源软件工具，包括Apache Hadoop，所有必需的软件可以免费下载和安装。软件要求包括：Windows 7及以上、Mac OS X 10.10及以上、Ubuntu 14.04及以上或CentOS 6及以上的虚拟机。 课程大纲： 1. 大数据：为何与从哪里 - 讨论数据的本质以及“大”数据的来源。 2. 数据科学：从大数据中获取价值 - 引入5步数据科学问题处理流程，强调大数据对公司、生活与世界的价值。 3. 系统：开始使用Hadoop - 讲解Hadoop和MapReduce的详细信息，并进行实际的MapReduce任务，借助Cloudera虚拟机进行“实践学习”。 通过本课程，您将能够更好地理解大数据及其带来的机遇，为您的职业发展打下坚实的基础。
Part: 1
Title:Big Data: Why and Where
Description:Data -- it's been around (even digitally) for a while. What makes data "big" and where does this big data come from?
Part: 2
Title:Data Science: Getting Value out of Big Data
Description:We love science and we love computing, don't get us wrong. But the reality is we care about Big Data because it can bring value to our companies, our lives, and the world. In this module we'll introduce a 5 step process for approaching data science problems.
Part: 3
Title:Systems: Getting Started with Hadoop
Description:Let's look at some details of Hadoop and MapReduce. Then we'll go "hands on" and actually perform a simple MapReduce task in the Cloudera VM. Pay attention - as we'll guide you in "learning by doing" in diagramming a MapReduce task as a Peer Review.
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
