Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data

Table of contents

1. Cover Page
2. Title Page
3. Copyright
4. Credits
5. About the Key Contributors
6. Acknowledgments
7. Contents
8. Foreword
9. Introduction
   1. EMC Academic Alliance
   2. EMC Proven Professional Certification
10. 1: Introduction to Big Data Analytics
    1. 1.1 Big Data Overview
    2. 1.2 State of the Practice in Analytics
    3. 1.3 Key Roles for the New Big Data Ecosystem
    4. 1.4 Examples of Big Data Analytics
    5. Summary
    6. Exercises
    7. Bibliography
11. 2: Data Analytics Lifecycle
    1. 2.1 Data Analytics Lifecycle Overview
    2. 2.2 Phase 1: Discovery
    3. 2.3 Phase 2: Data Preparation
    4. 2.4 Phase 3: Model Planning
    5. 2.5 Phase 4: Model Building
    6. 2.6 Phase 5: Communicate Results
    7. 2.7 Phase 6: Operationalize
    8. 2.8 Case Study: Global Innovation Network and Analysis (GINA)
    9. Summary
    10. Exercises
    11. Bibliography
12. 3: Review of Basic Data Analytic Methods Using R
    1. 3.1 Introduction to R
    2. 3.2 Exploratory Data Analysis
    3. 3.3 Statistical Methods for Evaluation
    4. Summary
    5. Exercises
    6. Bibliography
13. 4: Advanced Analytical Theory and Methods: Clustering
    1. 4.1 Overview of Clustering
    2. 4.2 K-means
    3. 4.3 Additional Algorithms
    4. Summary
    5. Exercises
    6. Bibliography
14. 5: Advanced Analytical Theory and Methods: Association Rules
    1. 5.1 Overview
    2. 5.2 Apriori Algorithm
    3. 5.3 Evaluation of Candidate Rules
    4. 5.4 Applications of Association Rules
    5. 5.5 An Example: Transactions in a Grocery Store
    6. 5.6 Validation and Testing
    7. 5.7 Diagnostics
    8. Summary
    9. Exercises
    10. Bibliography
15. 6: Advanced Analytical Theory and Methods: Regression
    1. 6.1 Linear Regression
    2. 6.2 Logistic Regression
    3. 6.3 Reasons to Choose and Cautions
    4. 6.4 Additional Regression Models
    5. Summary
    6. Exercises
16. 7: Advanced Analytical Theory and Methods: Classification
    1. 7.1 Decision Trees
    2. 7.2 Naïve Bayes
    3. 7.3 Diagnostics of Classifiers
    4. 7.4 Additional Classification Methods
    5. Summary
    6. Exercises
    7. Bibliography
17. 8: Advanced Analytical Theory and Methods: Time Series Analysis
    1. 8.1 Overview of Time Series Analysis
    2. 8.2 ARIMA Model
    3. 8.3 Additional Methods
    4. Summary
    5. Exercises
18. 9: Advanced Analytical Theory and Methods: Text Analysis
    1. 9.1 Text Analysis Steps
    2. 9.2 A Text Analysis Example
    3. 9.3 Collecting Raw Text
    4. 9.4 Representing Text
    5. 9.5 Term Frequency—Inverse Document Frequency (TFIDF)
    6. 9.6 Categorizing Documents by Topics
    7. 9.7 Determining Sentiments
    8. 9.8 Gaining Insights
    9. Summary
    10. Exercises
    11. Bibliography
19. 10: Advanced Analytics— Technology and Tools: MapReduce and Hadoop
    1. 10.1 Analytics for Unstructured Data
    2. 10.2 The Hadoop Ecosystem
    3. 10.3 NoSQL
    4. Summary
    5. Exercises
    6. Bibliography
20. 11: Advanced Analytics— Technology and Tools: In-Database Analytics
    1. 11.1 SQL Essentials
    2. 11.2 In-Database Text Analysis
    3. 11.3 Advanced SQL
    4. Summary
    5. Exercises
    6. Bibliography
21. 12: The Endgame, or Putting It All Together
    1. 12.1 Communicating and Operationalizing an Analytics Project
    2. 12.2 Creating the Final Deliverables
    3. 12.3 Data Visualization Basics
    4. Summary
    5. Exercises
    6. References and Further Reading
    7. Bibliography
22. Index