This book is designed for use in courses on big data Analytics at undergraduate/postgraduate level, particularly designed for the structured curriculum of Bachelor of arts and science.
Although the content of the book follows the essential content of complete concepts of big data in sufficiently broad scope and rigorous in coverage to satisfy any undergraduate and postgraduate requirements in the field of CSE/IT.
The book is organised into six chapters.
Chapter 1 contains the Introduction to Big Data Platform, structured vs. Unstructured Data, Challenges of Conventional Systems, Basic Definitions for Big Data, Intelligent Data Analysis, Importance of IDA, Analysis vs Reporting, Modern Data Analytic Tools.
Chapter 2 contains Introduction to Streams Concepts, Overview of Stream Data Processing, Examples of Stream Sources, Characteristics of Data Streams, Stream Data Model and Architecture, DBMS vs DSMS, Stream Queries, Issues in Stream Processing, Sampling Data in a Stream, Bloom Filter, False Positive Analysis, Applications of the Bloom Filter, FM algorithm, Real time Analytics Platform (RTAP) Applications.
Chapter 3 contains Data Visualization, Types of Charts, Different applications of data visualisation, K-means clustering, Association Rules mining, Apriori Algorithm, Regression Analysis, Types of Regression, Linear Regression, Logistic Regression, Linear Regression vs Logistic Regression, Naïve Bayes Classifier Algorithm, Decision Tree Classification Algorithm, Time Series Analysis & Forecasting, Text analysis and Stages in text analysis.
Chapter 4 contains Hadoop, Hadoop Architecture, Hadoop Distributed File System, History of Hadoop, HDFS, Important features and goals of HDFS, YARN, MapReduce and Phases of MapReduce data flow.
Chapter 5 contains Data Visualization, the types of Data Visualization, The various methods to Visualize Data, The Interactive Data Visualization, Use Cases of Data Visualization, Methods of Stock Market Prediction, Quantitative Technical Analysis.
Chapter 6 contains WALMART, Uber’s Unintended Burdens, Netflix leverage big data analytics, eBay’s e-business model and successful, Procter & Gamble, Big Data Challenges in the Tourism Industry, Big Data Is Used in the Travel Industry, Big Data in Travel Software Development and Big Data in Telecom Industry.
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