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Data Analytics

Radha Shankarmani

ISBN: 9788126560639

336 pages

Exclusively distributed by Technical Publications 

Description

 

Preface

About the Authors

Syllabus

Contents

 

Chapter 1 Introduction to Big Data

1.1 Introduction

1.2 Big Data Characteristics

1.3 Types of Big Data

1.4 Challenges of Traditional Systems

1.5 Web Data

1.6 Evolution of Analytic Scalability

1.7 When to use OLTP, MPP and Hadoop?

1.8 Grid Computing

1.9 Cloud Computing

1.10 MapReduce

1.11 Fault Tolerance

1.12 Analytic Processes and Tools

1.13 Analysis Versus Reporting

1.14 Statistical Concepts

 

Chapter 2 Data Analysis

2.1 Introduction

2.2 Data Analysis

2.3 Importance of Data Analysis

2.4 Data Analytics Applications

2.5 Regression Modelling Techniques

2.6 Bayesian Modelling, Inference and Bayesian Networks

2.7 Support Vector Machines and Kernel Methods

2.8 Time Series Analysis

2.9 Rule Induction

2.10 Sequential Cover Algorithm

 

Chapter 3 Neural Networks

3.1 Biological Neuron

3.2 Learning and Generalization

3.3 Competitive Learning

3.5 Fuzzy Logic

 

Chapter 4 Mining Data Streams

4.1 Introduction

4.2 Data Stream Management Systems

4.3 Data Stream Mining

4.4 Examples of Data Stream Applications

4.5 Stream Queries

4.6 Issues in Data Stream Query Processing

4.7 Sampling in Data Streams

4.8 Filtering Streams

4.9 Counting Distinct Elements in a Stream

4.10 Estimating Moments

4.11 Querying on Windows − Counting Ones in a Window

4.12 Decaying Windows

4.13 Real-Time Analytics Platform (RTAP)

 

Chapter 5 Frequent Itemsets and Clustering

5.1 Introduction to Frequent Itemsets

5.2 Market-Basket Model

5.3 Algorithm for Finding Frequent Itemsets

5.4 Handling Larger Datasets in Main Memory

5.5 Limited Pass Algorithms

5.6 Counting Frequent Items in a Stream

5.7 Introduction to Clustering

5.8 Overview of Clustering Techniques

5.9 Hierarchical Clustering

5.10 Partitioning Methods

5.11 The CURE Algorithm

5.12 Clustering High-Dimensional Data

5.13 CLIQUE

5.14 Frequent Pattern-Based Clustering Methods

5.15 Clustering Streams

 

Chapter 6 Frameworks and Visualization

6.1 Introduction

6.2 Introduction to Hadoop

6.3 What is Hadoop?

6.4 Core Components of Hadoop

6.5 Hadoop Ecosystem

6.6 Physical Architecture

6.7 Hadoop Limitations

6.8 Hive

6.9 MapReduce and The New Software Stack

6.10 MapReduce

6.11 Algorithms Using MapReduce

6.12 What is NoSQL?

6.13 NoSQL Business Drivers

6.14 NoSQL Case Studies

6.15 NoSQL Data Architectural Patterns

6.16 Variations of NoSQL Architectural Patterns

6.17 Using NoSQL to Manage Big Data

6.18 Visualizations

 

Summary

Review Questions