TY  - BOOK
AU  - EMC Education Services 
TI  - Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data
SN  - 9788126556533
U1  - 006.312 DAT 
PY  - 2015///
CY  - New Delhi
PB  - Wiley India Pvt. Ltd
KW  -  	      Data mining.     Big data.     Quantitative research.
N1  - 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
N2  - 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.
ER  -