Data science & big data analytics : discovering, analyzing, visualizing and presenting data / EMC Education Services.

Services, EMC, author.
EMC Education Services, author.
1st edition
Indianapolis, Indiana : John Wiley & Sons, 2015.
XVIII, 410 s. ill.
Data mining.
Big data.
Electronic books.
System Details:
text file
Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science teamDeploy a structured lifecycle approach to data analytics problemsApply appropriate analytic techniques and
Cover; Introduction; EMC Academic Alliance; EMC Proven Professional Certification; 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; Summary; Exercises; Bibliography; 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)Summary; Exercises; Bibliography; 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; Summary; Exercises; Bibliography; Chapter 4: Advanced Analytical Theory and Methods: Clustering; 4.1 Overview of Clustering; 4.2 K-means; 4.3 Additional Algorithms; Summary; Exercises; Bibliography; 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 Rules5.5 An Example: Transactions in a Grocery Store; 5.6 Validation and Testing; 5.7 Diagnostics; Summary; Exercises; Bibliography; 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; Summary; Exercises; 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; Summary; Exercises; Bibliography
Chapter 8: Advanced Analytical Theory and Methods: Time Series Analysis8.1 Overview of Time Series Analysis; 8.2 ARIMA Model; 8.3 Additional Methods; Summary; Exercises; 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; Summary; Exercises; Bibliography; Chapter 10: Advanced Analytics-Technology and Tools: MapReduce and Hadoop
10.1 Analytics for Unstructured Data10.2 The Hadoop Ecosystem; 10.3 NoSQL; Summary; Exercises; Bibliography; Chapter 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; 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; End User License Agreement
Description based upon print version of record.
Includes bibliographical references at the end of each chapters.
Description based on online resource; title from PDF title page (ebrary, viewed December 13, 2016).
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