Data Science and Big Data Analytics

474 Pages·2015·12.5 MB·English

Table of Contents
1. Introduction
1. EMC Academic Alliance
2. EMC Proven Professional Certification
2. Chapter 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
3. Chapter 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
4. Chapter 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
5. Chapter 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
6. Chapter 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
7. Chapter 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
8. Chapter 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
9. Chapter 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
10. Chapter 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
11. Chapter 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
12. Chapter 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
13. Chapter 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
14. End User License Agreement
List of Illustrations

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