Data Science and Big Data Analytics

Discovering, Analyzing, Visualizing and Presenting Data
Herausgeber: Emc Education Services

• Gebundenes Buch

Produktbeschreibung

Big data has been described as the "new oil." Data Science and Big Data Analytics is all about harnessing the power of data for new insights. EMC, the world class information management company, has developed this book with you in mind--every concept and task can be completed using free or open-source software. This is also an approved study guide for the EMC Data Science Associate (EMCDSA) certification. You'll learn everything you need to participate in big data projects, including how to:
* Become an immediate contributor on a data science team
* Reframe a business challenge as an analytics challenge
* Deploy a structured lifecycle approach to data analytics problems
* Apply appropriate analytic techniques and tools to analyze big data
* Learn how to tell a compelling story with data to drive business action
* Use open source tools such as R, Hadoop, and PostgreSQL
* Prepare for EMC Proven Professional Data Scientist certification

Today's IT professionals, business analysts, and database administrators are expected to work with enormous datasets. After reading Data Science and Big Data Analytics, you'll be on the cutting edge of this exciting paradigm shift.

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Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software.
This book will help you:

Become a contributor on a data science team
Deploy a structured lifecycle approach to data analytics problems
Apply appropriate analytic techniques and tools to analyzing big data
Learn how to tell a compelling story with data to drive business action
Prepare for EMC Proven Professional Data Science Certification
Corresponding data sets are available at www.wiley.com/go/9781118876138.

Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

Produktdetails

• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 432
• Erscheinungstermin: 17. März 2015
• Abmessung: 242mm x 197mm x 27mm
• Gewicht: 1184g
• ISBN-13: 9781118876138
• ISBN-10: 111887613X
• Artikelnr.: 41251340

Autorenporträt

EMC is a global leader in enabling businesses and service providers to transform their operations and deliver IT as a service. Fundamental to this transformation is cloud computing. Through innovative products and services, EMC accelerates the journey to cloud computing, helping IT departments to store, manage, protect and analyze their most valuable asset -- information -- in a more agile, trusted and cost-efficient way. Additional information about EMC can be found at www.EMC.com

Inhaltsangabe

Introduction xvii Chapter 1 * Introduction to Big Data Analytics 1 1.1 Big
Data Overview 2 1.1.1 Data Structures 5 1.1.2 Analyst Perspective on Data
Repositories 9 1.2 State of the Practice in Analytics 11 1.2.1 BI Versus
Data Science 12 1.2.2 Current Analytical Architecture 13 1.2.3 Drivers of
Big Data 15 1.2.4 Emerging Big Data Ecosystem and a New Approach to
Analytics 16 1.3 Key Roles for the New Big Data Ecosystem 19 1.4 Examples
of Big Data Analytics 22 Summary 23 Exercises 23 Bibliography 24 Chapter 2
* Data Analytics Lifecycle 25 2.1 Data Analytics Lifecycle Overview 26
2.1.1 Key Roles for a Successful Analytics Project 26 2.1.2 Background and
Overview of Data Analytics Lifecycle 28 2.2 Phase 1: Discovery 30 2.2.1
Learning the Business Domain 30 2.2.2 Resources 31 2.2.3 Framing the
Problem 32 2.2.4 Identifying Key Stakeholders 33 2.2.5 Interviewing the
Analytics Sponsor 33 2.2.6 Developing Initial Hypotheses 35 2.2.7
Identifying Potential Data Sources 35 2.3 Phase 2: Data Preparation 36
2.3.1 Preparing the Analytic Sandbox 37 2.3.2 Performing ETLT 38 2.3.3
Learning About the Data 39 2.3.4 Data Conditioning 40 2.3.5 Survey and
Visualize 41 2.3.6 Common Tools for the Data Preparation Phase 42 2.4 Phase
3: Model Planning 42 2.4.1 Data Exploration and Variable Selection 44 2.4.2
Model Selection 45 2.4.3 Common Tools for the Model Planning Phase 45 2.5
Phase 4: Model Building 46 2.5.1 Common Tools for the Model Building Phase
48 2.6 Phase 5: Communicate Results 49 2.7 Phase 6: Operationalize 50 2.8
Case Study: Global Innovation Network and Analysis (GINA) 53 2.8.1 Phase 1:
Discovery 54 2.8.2 Phase 2: Data Preparation 55 2.8.3 Phase 3: Model
Planning 56 2.8.4 Phase 4: Model Building 56 2.8.5 Phase 5: Communicate
Results 58 2.8.6 Phase 6: Operationalize 59 Summary 60 Exercises 61
Bibliography 61 Chapter 3 * Review of Basic Data Analytic Methods Using R
63 3.1 Introduction to R 64 3.1.1 R Graphical User Interfaces 67 3.1.2 Data
Import and Export 69 3.1.3 Attribute and Data Types 71 3.1.4 Descriptive
Statistics 79 3.2 Exploratory Data Analysis 80 3.2.1 Visualization Before
Analysis 82 3.2.2 Dirty Data 85 3.2.3 Visualizing a Single Variable 88
3.2.4 Examining Multiple Variables 91 3.2.5 Data Exploration Versus
Presentation 99 3.3 Statistical Methods for Evaluation 101 3.3.1 Hypothesis
Testing 102 3.3.2 Difference of Means 104 3.3.3 Wilcoxon Rank-Sum Test 108
3.3.4 Type I and Type II Errors 109 3.3.5 Power and Sample Size 110 3.3.6
ANOVA 110 Summary 114 Exercises 114 Bibliography115 Chapter 4 * Advanced
Analytical Theory and Methods: Clustering 117 4.1 Overview of Clustering
118 4.2 K-means 118 4.2.1 Use Cases 119 4.2.2 Overview of the Method 120
4.2.3 Determining the Number of Clusters 123 4.2.4 Diagnostics 128 4.2.5
Reasons to Choose and Cautions 130 4.3 Additional Algorithms 134 Summary
135 Exercises 135 Bibliography 136 Chapter 5 * Advanced Analytical Theory
and Methods: Association Rules 137 5.1 Overview 138 5.2 Apriori Algorithm
140 5.3 Evaluation of Candidate Rules 141 5.4 Applications of Association
Rules 143 5.5 An Example: Transactions in a Grocery Store 143 5.5.1 The
Groceries Dataset 144 5.5.2 Frequent Itemset Generation 146 5.5.3 Rule
Generation and Visualization 152 5.6 Validation and Testing 157 5.7
Diagnostics 158 Summary 158 Exercises 159 Bibliography 160 Chapter 6 *
Advanced Analytical Theory and Methods: Regression 161 6.1 Linear
Regression 162 6.1.1 Use Cases 162 6.1.2 Model Description 163 6.1.3
Diagnostics 173 6.2 Logistic Regression178 6.2.1 Use Cases 179 6.2.2 Model
Description 179 6.2.3 Diagnostics 181 6.3 Reasons to Choose and Cautions
188 6.4 Additional Regression Models 189 Summary 190 Exercises 190 Chapter
7 * Advanced Analytical Theory and Methods: Classification 191 7.1 Decision
Trees 192 7.1.1 Overview of a Decision Tree 193 7.1.2 The General Algorithm
197 7.1.3 Decision Tree Algorithms 203 7.1.4 Evaluating a Decision Tree 204
7.1.5 Decision Trees in R 206 7.2 Naïve Bayes 211 7.2.1 Bayes' Theorem 212
7.2.2 Naïve Bayes Classifier 214 7.2.3 Smoothing 217 7.2.4 Diagnostics 217
7.2.5 Naïve Bayes in R 218 7.3 Diagnostics of Classifiers 224 7.4
Additional Classification Methods 228 Summary 229 Exercises 230
Bibliography 231 Chapter 8 * Advanced Analytical Theory and Methods: Time
Series Analysis 233 8.1 Overview of Time Series Analysis 234 8.1.1
Box-Jenkins Methodology 235 8.2 ARIMA Model 236 8.2.1 Autocorrelation
Function (ACF) 236 8.2.2 Autoregressive Models 238 8.2.3 Moving Average
Models 239 8.2.4 ARMA and ARIMA Models 241 8.2.5 Building and Evaluating an
ARIMA Model 244 8.2.6 Reasons to Choose and Cautions 252 8.3 Additional
Methods 253 Summary 254 Exercises 254 Chapter 9 * Advanced Analytical
Theory and Methods: Text Analysis 255 9.1 Text Analysis Steps 257 9.2 A
Text Analysis Example 259 9.3 Collecting Raw Text 260 9.4 Representing Text
264 9.5 Term Frequency--Inverse Document Frequency (TFIDF) 269 9.6
Categorizing Documents by Topics 274 9.7 Determining Sentiments 277 9.8
Gaining Insights 283 Summary 290 Exercises 290 Bibliography 291 Chapter 10
* Advanced Analytics--Technology and Tools: MapReduce and Hadoop 295 10.1
Analytics for Unstructured Data 296 10.1.1 Use Cases 296 10.1.2 MapReduce
298 10.1.3 Apache Hadoop 300 10.2 The Hadoop Ecosystem 306 10.2.1 Pig 306
10.2.2 Hive 308 10.2.3 HBase 311 10.2.4 Mahout 319 10.3 NoSQL 322 Summary
323 Exercises 324 Bibliography 324 Chapter 11 * Advanced
Analytics--Technology and Tools: In-Database Analytics 327 11.1 SQL
Essentials 328 11.1.1 Joins 330 11.1.2 Set Operations 332 11.1.3 Grouping
Extensions 334 11.2 In-Database Text Analysis 338 11.3 Advanced SQL 343
11.3.1 Window Functions 343 11.3.2 User-Defined Functions and Aggregates
347 11.3.3 Ordered Aggregates 351 11.3.4 MADlib 352 Summary 356 Exercises
356 Bibliography 357 Chapter 12 * The Endgame, or Putting It All Together
359 12.1 Communicating and Operationalizing an Analytics Project 360 12.2
Creating the Final Deliverables 362 12.2.1 Developing Core Material for
Multiple Audiences 364 12.2.2 Project Goals 365 12.2.3 Main Findings 367
12.2.4 Approach 369 12.2.5 Model Description 371 12.2.6 Key Points
Supported with Data 372 12.2.7 Model Details 372 12.2.8 Recommendations 374
12.2.9 Additional Tips on Final Presentation 375 12.2.10 Providing
Technical Specifications and Code 376 12.3 Data Visualization Basics 377
12.3.1 Key Points Supported with Data 378 12.3.2 Evolution of a Graph 380
12.3.3 Common Representation Methods 386 12.3.4 How to Clean Up a Graphic
387 12.3.5 Additional Considerations 392 Summary 393 Exercises 394
References and Further Reading 394 Bibliography 394 Index 397