第1章大數(shù)據(jù)概述 案例：谷歌流感趨勢（GFT） 學(xué)習(xí)目標(biāo) 1.1大數(shù)據(jù)概述 1.1.1大數(shù)據(jù)的定義 1.1.2大數(shù)據(jù)的解析 1.1.3大數(shù)據(jù)的來源 1.1.4大數(shù)據(jù)的特點 1.1.5數(shù)據(jù)驅(qū)動型業(yè)務(wù)問題分類 1.2大數(shù)據(jù)分析概述 1.2.1大數(shù)據(jù)分析的發(fā)展 1.2.2大數(shù)據(jù)分析的界定 1.2.3大數(shù)據(jù)分析的優(yōu)點 1.2.4大數(shù)據(jù)分析的不足 1.2.5大數(shù)據(jù)分析的關(guān)鍵問題 1.3大數(shù)據(jù)的正確認(rèn)知 1.4大數(shù)據(jù)面臨的挑戰(zhàn) 1.5大數(shù)據(jù)應(yīng)用的七個關(guān)鍵步驟 1.6大數(shù)據(jù)的實際應(yīng)用 1.7大數(shù)據(jù)與相關(guān)學(xué)科的關(guān)系 案例延續(xù)：谷歌流感趨勢（GFT） 第二章大數(shù)據(jù)的技術(shù)基礎(chǔ) 案例：Fortnite如何利用分析和云來分析PB級的游戲數(shù)據(jù) 學(xué)習(xí)目標(biāo) 2.1大數(shù)據(jù)的基本框架 2.1.1數(shù)據(jù)用戶的分析活動 2.1.2大數(shù)據(jù)基本架構(gòu) 2.1.3規(guī)劃大數(shù)據(jù)基本架構(gòu) 2.1.4大數(shù)據(jù)基本架構(gòu)面臨的挑戰(zhàn) 2.1.5構(gòu)建成功大數(shù)據(jù)基本架構(gòu)的關(guān)鍵要素 2.2 Hadoop 2.2.1 Hadoop的歷史 2.2.2 Hadoop的架構(gòu) 2.2.3 Hadoop的優(yōu)勢 2.2.4 Hadoop的實現(xiàn) 2.2.5 Hadoop生態(tài)系統(tǒng) 2.3硬盤 2.3.1 HDFS概述 2.3.2 HDFS架構(gòu) 2.3.3 HDFS的特點 2.4 MapReduce 2.4.1 MapReduce概述 2.4.2 MapReduce的發(fā)展階段 2.5 NoSQL 2.5.1 NoSQL數(shù)據(jù)庫的定義 2.5.2 NOSQL數(shù)據(jù)庫的類型 2.5.3 NoSQL數(shù)據(jù)庫的歷史 2.5.4 NoSQL數(shù)據(jù)庫的優(yōu)缺點 2.6 HBase 2.6.1 HBase概述 2.6.3 HBase和HDFS 2.6.3 HBase架構(gòu) 2.7云服務(wù) 2.7.1云服務(wù)概述 2.7.2云服務(wù)的特點 2.7.3云服務(wù)部署模型 2.7.4常見的云服務(wù)模型 案例延續(xù)：世界知名公司采用的大數(shù)據(jù)基本架構(gòu) 第3章 大數(shù)據(jù)采集和存儲 案例：心血管疾病臨床數(shù)據(jù)管理系統(tǒng) 學(xué)習(xí)目標(biāo) 3.1大數(shù)據(jù)采集概述 3.1.1大數(shù)據(jù)采集的定義 3.1.2大數(shù)據(jù)采集的數(shù)據(jù)類型 3.1.3大數(shù)據(jù)采集的特點 3.2大數(shù)據(jù)采集的挑戰(zhàn)與趨勢 3.2.1大數(shù)據(jù)采集面臨的新挑戰(zhàn) 3.2.2大數(shù)據(jù)采集的未來需求和新興趨勢 3.3大數(shù)據(jù)采集的方法和途徑 3.3.1大數(shù)據(jù)采集來源 3.3.2大數(shù)據(jù)采集源的分類 3.4大數(shù)據(jù)采集工具 3.5大數(shù)據(jù)存儲概述 3.6大數(shù)據(jù)存儲的新架構(gòu)和系統(tǒng) 案例延續(xù)：臨床數(shù)據(jù)管理系統(tǒng)的需求和架構(gòu) 第4章大數(shù)據(jù)預(yù)處理 案例：大數(shù)據(jù)的“臟”問題 學(xué)習(xí)目標(biāo) 4.1大數(shù)據(jù)質(zhì)量概述 4.1.1大數(shù)據(jù)質(zhì)量的定義 4.1.2大數(shù)據(jù)質(zhì)量的歷史 4.1.3大數(shù)據(jù)時代數(shù)據(jù)質(zhì)量的挑戰(zhàn) 4.1.4大數(shù)據(jù)的質(zhì)量標(biāo)準(zhǔn) 4.2大數(shù)據(jù)清理概述 4.2.1大數(shù)據(jù)清理的定義 4.2.2大數(shù)據(jù)清理框架 4.2.3大數(shù)據(jù)清理常見問題 4.3大數(shù)據(jù)清理流程 4.3.1基于ETL過程 4.3.2基于PAIAM模型 4.3.3基于質(zhì)量評估的視角 4.4大數(shù)據(jù)清理的輔助手段 案例延續(xù)：Lambda架構(gòu)和大數(shù)據(jù)質(zhì)量 第5章 數(shù)據(jù)挖掘 案例：尿布和啤酒 學(xué)習(xí)目標(biāo) 5.1數(shù)據(jù)挖掘?qū)д? 5.1.1數(shù)據(jù)挖掘的歷史 5.1.2數(shù)據(jù)挖掘的定義 5.1.3數(shù)據(jù)挖掘的特點 5.1.4數(shù)據(jù)挖掘的學(xué)習(xí)策略 5.1.5數(shù)據(jù)挖掘的價值 5.1.6數(shù)據(jù)挖掘問題 5.1.7數(shù)據(jù)挖掘的新趨勢 5.2大數(shù)據(jù)與數(shù)據(jù)挖掘 5.2.1大數(shù)據(jù)與數(shù)據(jù)挖掘的區(qū)別與聯(lián)系 5.2.2大數(shù)據(jù)挖掘概述 5.3數(shù)據(jù)挖掘過程模型 5.4數(shù)據(jù)挖掘任務(wù) 案例延續(xù)：尿布和啤酒的起源 第6章 數(shù)據(jù)可視化 案例：消防安全行業(yè)的數(shù)據(jù)可視化 學(xué)習(xí)目標(biāo) 6.1數(shù)據(jù)可視化介紹 6.1.1數(shù)據(jù)可視化的背景 6.1.2數(shù)據(jù)可視化的定義 6.1.3數(shù)據(jù)可視化的挑戰(zhàn) 6.1.4數(shù)據(jù)可視化的特點 6.1.5數(shù)據(jù)可視化的應(yīng)用 6.2數(shù)據(jù)可視化過程 6.2.1一般可視化過程 6.2.2其他可視化過程 6.2.3可視化過程指南 6.3大數(shù)據(jù)時代的數(shù)據(jù)可視化工具 6.4大數(shù)據(jù)時代的數(shù)據(jù)可視化方法 6.4.1數(shù)據(jù)可視化的常用方法 6.4.2大數(shù)據(jù)時代的數(shù)據(jù)可視化方法 6.5. 如何設(shè)計數(shù)據(jù)可視化 6.5.1數(shù)據(jù)可視化需求 6.5.2設(shè)計數(shù)據(jù)可視化的關(guān)鍵要素 6.6如何改進(jìn)數(shù)據(jù)可視化設(shè)計 6.6.1數(shù)據(jù)可視化改進(jìn)原則 6.6.2數(shù)據(jù)可視化改進(jìn)提示 案例延續(xù)：在線對話空間的視覺探索 第7章大數(shù)據(jù)分析專業(yè)報告 案例：馬薩諸塞州大數(shù)據(jù)報告 學(xué)習(xí)目標(biāo) 7.1大數(shù)據(jù)分析報告概述 7.1.1五種數(shù)據(jù)關(guān)系 7.1.2四種大數(shù)據(jù)分析報告 7.1.3大數(shù)據(jù)分析報告提示 7.2數(shù)據(jù)分析報告模板 7.2.1 PPT中的數(shù)據(jù)分析報告模板 7.2.2 WORD格式的數(shù)據(jù)分析報告模板 7.2.3其他地方的數(shù)據(jù)分析報告模板 7.3數(shù)據(jù)分析報告的錯誤和修復(fù) 案例延續(xù)：大數(shù)據(jù)分析的辯證認(rèn)識 第八章庫齡與庫存分析 8.1培訓(xùn)背景 8.2培訓(xùn)介紹 8.2.1原始數(shù)據(jù)情況 8.2.2培訓(xùn)分析過程 8.3培訓(xùn)過程 8.3.1創(chuàng)建新項目 8.3.2數(shù)據(jù)導(dǎo)入 8.3.3數(shù)據(jù)分析 8.3.4數(shù)據(jù)可視化 8.4培訓(xùn)總結(jié) 8.5實踐思維問題 第9章銷售數(shù)據(jù)分析 9.1培訓(xùn)背景 9.2培訓(xùn)介紹 9.2.1原始數(shù)據(jù)情況 9.2.2培訓(xùn)分析過程 9.3培訓(xùn)流程 9.3.1創(chuàng)建新項目 9.3.2數(shù)據(jù)導(dǎo)入 9.3.3數(shù)據(jù)分析 9.3.4數(shù)據(jù)可視化 9.4培訓(xùn)總結(jié) 9.4.1培訓(xùn)結(jié)論總結(jié) 9.4.2培訓(xùn)總結(jié)建議 9.5實踐思維問題 Chapter 1 Overview of big data Case: Google Flu Trends (GFT) Learning objectives 1.1 Introduction to big data 1.1.1 Definition of big data 1.1.2 Understanding to big data 1.1.3 Sources of big data 1.1.4 The characteristics of big data 1.1.5 Classifying business problems according to big data type 1.2 Introduction to big data analytics 1.2.1 Development of big data analytics 1.2.2 Definition of big data analytics 1.2.3 Benefits of big data analytics 1.2.4 Shortcomings of big data analytics 1.2.5 Key questions to ask of any data analysis 1.3 Understandings of big data 1.4 Challenges for big data 1.5 The Seven key steps of big data analytics 1.6 Application of big data 1.7 Relationship between big data and related disciplines Case continuation: Google Flu Trends (GFT) Chapter 2 Technical foundations of big data Case: How Fortnite Approaches Analytics, Cloud to Analyze Petabytes of Game Data Learning target 2.1 Basic framework for big data 2.1.1 Activities of data users 2.1.2 Architecture of big data 2.1.3 Planning the big data analysis architecture 2.1.4 Challenges for big data analysis architecture 2.1.5 Key elements for building a successful big data analysis architecture 2.2 Hadoop 2.2.1 History of Hadoop 2.2.2 Hadoop architecture 2.2.3 Advantages of Hadoop 2.2.4 Implement of Hadoop 2.2.5 The Hadoop Ecosystem 2.3 HDFS 2.3.1 HDFS overview 2.3.2 HDFS architecture 2.3.3 Features of HDFS 2.4 MapReduce 2.4.1 MapReduce overview 2.4.2 Stages of MapReduce 2.5 NoSQL 2.5.1 Definition of NoSQL database 2.5.2 TYPES OF NOSQL databases 2.5.3 History of NoSQL database 2.5.4 The advantages and disadvantages of NoSQL database 2.6 HBase 2.6.1 HBase overview 2.6.3 HBase and HDFS 2.6.3 HBase architecture 2.7 Cloud services 2.7.1 Cloud services overview 2.7.2 Characteristics of cloud service 2.7.3 Deployment models of cloud service 2.7.4 Common cloud service models Case continuation: Data Analytics Architecture Adopted by Famous Company in the World Chapter 3 Big data acquisition and storage Cases：Clinical Data Management System for Cardiovascular Disease Learning target 3.1 Overview of big data acquisition 3.1.1 Definition of big data acquisition 3.1.2 Data types for big data acquisition 3.1.3 Characteristics of big data acquisition 3.2 The future of big data acquisition 3.2.1 Emerging challenges for big data acquisition 3.2.2 Future requirements and emerging trends for big data acquisition 3.3 Methods and ways of big data acquisition 3.3.1 Sources of big data acquisition 3.3.2 A Taxonomy of big data acquisition sources 3.4 The tools of big data acquisition 3.5 Overview of big data storage 3.6 New architecture and system of big data storage Case continuation: Requirements and Architecture of Clinical Data Management System Chapter 4 Data processing Cases：Big data's dirty problem Learning target 4.1 Big data quality overview 4.1.1 Big data quality definitions 4.1.2 History of big data quality 4.1.3 The challenges of data qualityin the big data era 4.1.4 Quality criteria of big data 4.2 Big data cleaning overview 4.2.1 Big data cleaning definitions 4.2.2 Big data cleaning Framework 4.2.3 Big data cleaning problems 4.3 Big data cleaning process 4.3.1 Based on the ETL process 4.3.2 Based on PAIAM model 4.3.3 Based on the perspective of quality assessment 4.4 Auxiliary means of big data cleaning 4.4.1 Motivating factors of big data cleaning 4.4.2 Big data cleaning tools Case continuation: The Lambda architecture and big data quality Chapter 5 Data mining Case: Diapers and beer Learning objectives 5.1 Introduction to data mining 5.1.1 History of data mining 5.1.2 Definition of data mining 5.1.3 Character of data mining 5.1.4 Learning strategies of data mining 5.1.5 Value of data mining 5.1.6 Data mining issues 5.1.7 New trend in data mining 5.2 Big data and data mining 5.2.1 Difference and relation between big data and data mining 5.2.2 Big data mining overview 5.3 Data mining process model 5.4 Data mining tasks Case continuation: origins of diapers and beer Chapter 6 Data visualization Case: Data visualization in the fire safety industry Learning objectives 6.1 Introduction to data visualization 6.1.1 Background of data visualization 6.1.2 Definition of data visualization 6.1.3 Challenges for data visualization 6.1.4 Character of data visualization 6.1.5 Application of data visualization 6.2 Process of data visualization 6.2.1 General visualization process 6.2.2 Other visualization processes 6.2.3 Guide to visualization processes 6.3 Tool of data visualization in big data era 6.4 Methods of data visualization in big data era 6.4.1 Common way of data visualization 6.4.2 Methods of data visualization in big data era 6.5. How to design data visualization 6.5.1 Demand of data visualization 6.5.2 Key element of design data visualization 6.6 How to improve data visualization design 6.6.1 Principles of data visualization improvement 6.6.2 Tips of data visualization improvement Case continuation: A visual exploration of the online conversational space Chapter 7 Professional report on big data analytics Case: The Massachusetts Big Data Report Learning objectives 7.1 Overview of big data analytics report 7.1.1 Five types of data relationships 7.1.2 Four types of big data analytics report 7.1.3 Tips of big data analytics report 7.2 Data analysis report template 7.2.1 Data analysis report template in PPT 7.2.2 Data analysis report template in WORD 7.2.3 Data analysis report template in other places 7.3 Mistakes and fixes on data analysis report Case continuation: Dialectical Cognition of big data analytics Chapter 8 Analysis of stock age and inventory 8.1 Training Background 8.2 Training Introduction 8.2.1 Raw data situation 8.2.2 Analysis process of training 8.3 Training Process 8.3.1 Create a new project 8.3.2 Data import 8.3.3 Data analysis 8.3.4 Data Visualization 8.4 Training Summary 8.5 Practical Thinking Questions Chapter 9 Analysis of Sales Data 9.1 Training Background 9.2 Training Introduction 9.2.1 Raw data situation 9.2.2 Analysis process of training 9.3 Training Process 9.3.1 Create a new project 9.3.2 Data import 9.3.3 Data Analysis 9.3.4 Data Visualization 9.4 Training Summary 9.4.1 Summary of training conclusions 9.4.2 Training summary recommendations 9.5 Practical Thinking Questions