Data Science and Big Data Analytics · Discovering, Analyzing, Visualizing and Presenting Data by Services, EMC Education -- Read -- Imperial Library of Trantor

Index

Cover Page Title Page Copyright Credits About the Key Contributors Acknowledgments Contents Foreword Introduction

EMC Academic Alliance EMC Proven Professional Certification

1: Introduction to Big Data Analytics

1.1 Big Data Overview 1.2 State of the Practice in Analytics 1.3 Key Roles for the New Big Data Ecosystem 1.4 Examples of Big Data Analytics Summary Exercises Bibliography

2: Data Analytics Lifecycle

2.1 Data Analytics Lifecycle Overview 2.2 Phase 1: Discovery 2.3 Phase 2: Data Preparation 2.4 Phase 3: Model Planning 2.5 Phase 4: Model Building 2.6 Phase 5: Communicate Results 2.7 Phase 6: Operationalize 2.8 Case Study: Global Innovation Network and Analysis (GINA) Summary Exercises Bibliography

3: Review of Basic Data Analytic Methods Using R

3.1 Introduction to R 3.2 Exploratory Data Analysis 3.3 Statistical Methods for Evaluation Summary Exercises Bibliography

4: Advanced Analytical Theory and Methods: Clustering

4.1 Overview of Clustering 4.2 K-means 4.3 Additional Algorithms Summary Exercises Bibliography

5: Advanced Analytical Theory and Methods: Association Rules

5.1 Overview 5.2 Apriori Algorithm 5.3 Evaluation of Candidate Rules 5.4 Applications of Association Rules 5.5 An Example: Transactions in a Grocery Store 5.6 Validation and Testing 5.7 Diagnostics Summary Exercises Bibliography

6: Advanced Analytical Theory and Methods: Regression

6.1 Linear Regression 6.2 Logistic Regression 6.3 Reasons to Choose and Cautions 6.4 Additional Regression Models Summary Exercises

7: Advanced Analytical Theory and Methods: Classification

7.1 Decision Trees 7.2 Naïve Bayes 7.3 Diagnostics of Classifiers 7.4 Additional Classification Methods Summary Exercises Bibliography

8: Advanced Analytical Theory and Methods: Time Series Analysis

8.1 Overview of Time Series Analysis 8.2 ARIMA Model 8.3 Additional Methods Summary Exercises

9: Advanced Analytical Theory and Methods: Text Analysis

9.1 Text Analysis Steps 9.2 A Text Analysis Example 9.3 Collecting Raw Text 9.4 Representing Text 9.5 Term Frequency—Inverse Document Frequency (TFIDF) 9.6 Categorizing Documents by Topics 9.7 Determining Sentiments 9.8 Gaining Insights Summary Exercises Bibliography

10: Advanced Analytics— Technology and Tools: MapReduce and Hadoop

10.1 Analytics for Unstructured Data 10.2 The Hadoop Ecosystem 10.3 NoSQL Summary Exercises Bibliography

11: Advanced Analytics— Technology and Tools: In-Database Analytics

11.1 SQL Essentials 11.2 In-Database Text Analysis 11.3 Advanced SQL Summary Exercises Bibliography

12: The Endgame, or Putting It All Together

12.1 Communicating and Operationalizing an Analytics Project 12.2 Creating the Final Deliverables 12.3 Data Visualization Basics Summary Exercises References and Further Reading Bibliography

Index