About the Book

Data Science & Big Data Analytics educates readers about what Big Data is and how to extract value from it. The book covers methods and technologies required to analyze structured and unstructured datasets, as more individuals and organizations build out their capabilities to analyze Big Data and draw insights from it. Additional focus areas include machine learning, data visualization and presentation skills. The book provides practical foundation level training that enables immediate and effective participation in big data and other analytics projects. It provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools, including MapReduce and Hadoop.

About the Author

EMC employs approximately 65,000 people worldwide with 400 sales offices and scores of partners in 85 countries around the world and R&D centers in Belgium, Brazil, China, France, Ireland, India, Israel, the Netherlands, Russia, Singapore and the U.S. and manufacturing facilities in the U.S. and Ireland.  EMC ranks 152 in the Fortune 500. In a year when IT spending worldwide grew by approximately two percent, EMC consolidated revenue grew by nine percent in 2012, to a record $21.7 billion. 

Table of Contents:
Introduction  Chapter 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  Chapter 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) Chapter 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 Chapter 4 Advanced Analytical Theory and Methods: Clustering 4.1 Overview of Clustering  4.2 K-means 4.3 Additional Algorithms  Chapter 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   Chapter 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  Chapter 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 Chapter 8 Advanced Analytical Theory and Methods: Time Series Analysis  8.1 Overview of Time Series Analysis 8.2 ARIMA Model  8.3 Additional Methods   Chapter 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 Chapter 10 Advanced Analytics--Technology and Tools: MapReduce and Hadoop 10.1 Analytics for Unstructured Data 10.2 The Hadoop Ecosystem 10.3 NoSQL Chapter 11 Advanced Analytics--Technology and Tools: In-Database Analytics 11.1 SQL Essentials 11.2 In-Database Text Analysis 11.3 Advanced SQL Chapter 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