Data science and analytics / editors, Sneha Kumari, K.K. Tripathy, Vidya Kumbhar.

Format:

Book

Contributor:

Kumari, Sneha, 1988- editor.

Tripathy, Kamal K., 1973- editor.

Kumbhar, Vidya, 1980- editor.

Language:

English

Subjects (All):

Management--Statistical methods.

Management.

Physical Description:

1 online resource (xxii, 189 pages)

Edition:

1st ed.

Place of Publication:

Bingley, England : Emerald Publishing, [2021]

Summary:

Data Science and Analyticsexplores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications.

Contents:

Intro

Half Title Page

Title Page

Copyright Page

Acknowledgments

Contents

Preface

Five Vs of Big Data

Why is Big Data Important?

Application of Big Data and Business Analytics

Big Data and Business Analytics for Decision-Making

Objectives

Target Audience

Organization of this Book

Foreword

Editorial Advisory Board

List of Contributors

Editor Biographies

Chapter 1-Data Visualization

Introduction

Types of Data

Types of Visualizations

Graphical Visualizations

Software

Conclusion

Future

Definitions

Chapter 2-Analytical Aspects of Multimedia Big Data Computing and Futur

1 Introduction

2 Basics of BD

3 MMBD

Related Work in BD Computing

3.1 Features of MMBD

3.2 Life Cycle of MMBD

4 Importance of MMBD

5 Applications of MMBD

6 Open Problems Outlook and Research Opportunities of MMBD

Chapter 3-Predictive Analysis: Comprehensive Study of Popular Open-Source Tools

Objective

Background Details

1. Scikit-learn

2. WEKA

3. RStudio

4. KNIME

5. Orange

6. RapidMiner

7. Tool Summary

Methodology

1. Selection of Tools

2. Datasets

3. Classification

Results and Discussions

1. Experiment Setups and Preliminaries

Evaluating Performance of the Algorithms

a. WEKA. All algorithms executed successfully on WEKA for the given datasets. Table 4 shows the accuracy of some classification algorithms using WEKA tool in percentage:

b. Scikit-learn. Accuracy is also calculated using scikit software package installed in Python environment. Table 5 shows the accuracy of these classification techniques for the mentioned datasets using scikit-learn tool for the mentioned datasets.

c. RStudio. Table 6 shows the accuracy measures of various classification algorithms using RStudio. The dataset is split by 70% and 30% as training and testing dataset, respectively. Basic preprocessing on the dataset was done, and the classification accu

d. Orange. For Orange, no preprocessing widget was used. All classification techniques ran successfully on Orange tool. Table 7 shows the accuracy measures of various classification algorithms using Orange tool. It can be observed that Decision Tree Class

e. KNIME. For KNIME, it was found that the classifier algorithms could not be executed against the iris dataset since its dataset is a multiclass and the classifier is only able to deal with the binary classes.

f. RapidMiner

Overall Analysis

Key Terms and Definitions

Chapter 4-Market Opportunities Through Effective Market Analytics

Materials and Methods

Sample Selection and Design

Statistical Tools and Techniques:

Justification of Research Methodology

Results and Discussion

Market Segmentation of Consumer Market of Nylon Pauvaji Restaurants

Market Segmentation of Customers for Khetlaaapa Tea

Market Segmentations of Karnavati Dabeli

Market Segmentation of Dwarkadhish Tea

Market Segmentation of Santushti Ice-Cream Parlor

Understanding the Key Terms

Foreign Direct Investment (FDI)

Food Safety Standard Authority of India (FSSAI) Act, 2006

Visual Merchandising

Entrepreneurship

Goods and Services Tax (GST)

Standard Operating Procedure (SOP)

Hard Core Loyal

Chapter 5-Advance Stochastic Point Process Techniques: Modeling Problems in the Internet of Things (IoT) and Marketing

Section 1: Introduction

The Appropriateness of the Technique in Stochastic Modeling.

Section 2: Data Traffic Problems in IoT in the Context of Health-Care Problems Using Wireless Communication

Section 3: Prediction of Expected Number of Patients Undergoing Treatment at Any Time.

B. The Expected Cost of the Resources Required for Treating the Patients at any Time.

Section 4: Stochastic Time-Dependent Modeling of Customer Equity

A. Estimating the Customer Base of a Product

B. Estimating Customer Equity at any Time

Section 5: Discussion and Conclusion

Chapter 6-Real-Time Data Analytics - A Contemporary Approach Toward Customer Relationship Management

Theoretical Foundation

eCRM

Business Analytics Based on Customer Data

IoT-Based Real-Time Analytics

Customer Life Cycle Management with Real-Time Data Analytics

IoT Architecture

EDSOA Benefits

Discussion

Limitations

Chapter 7-Application of Big Data for Sustainable Rural Development with Special Reference to MGNREGA

Wage Goals Labor Employment Capital Wage Good

Research Methodology

Flow of this Chapter

Literature Review

Poverty and Unemployment in India

Rural Employment

Employment Programs

Wage Employment Programs

MGNREGA

Implementation Mechanism of MGNREGA

Implementation Status: Physical and Financial

Salient Feature of MGNREGA

Key Points of Salient Features

Big Data and MGNREGA

MIS

Number of Job Cards Deleted

Number of Registered Households and Persons

Cumulative Number of Households Issued Job Cards

Semi-Skilled Worker

Trend of Activities in MGNREGA

Work Demand Pattern

Average Wage Paid

MGNREGA Expenditure

Gender and Women Empowerment through Feminization of Rural Employment

Employment Demanded versus Employment Offered

Delay Payment

Comparison of Notified Wage and Agriculture Wage.

Pre- and Post-MGNREGA Wage Situation in Select States

Issues and Challenges in MGNREGA

Conclusion and Future Research Direction

Theoretical and Managerial Implications

Research Questions for Future Research

Chapter 8-Challenges of Digital Technologies in the Development of Supply Chains: A Guide for Their Selection

Implementing Digital Technologies

Becoming a Digital Champion

Digitalization. The digitalization process requires the alignment between the digital alternatives and the objectives of the supply chain. This does not mean having the latest digital technologies. Companies should be aware of the digital methodology that

Technology Implementation. In this stage, technology is implemented based on the digital methodology chosen in the previous stage. According to Büyüközkan and Göçer (2018), the first thing to do is determine the tasks to be performed and the necessary equ

Supply Chain Management. Even if the digitalization and technological implementation were done satisfactorily, they have to be properly managed. This is an essential process, as it helps to reach complex decisions that will allow achieving the strategic o

Strategic Implementation

Digital Technologies in the Supply Chain

BD

Robotics

Autonomous Vehicles

Additive Manufacturing (3D Printing)

AI

Discovering the Main Effects of Digital Technologies in the Supply Chain

Particular Benefits of Digital Technologies Applied to Supply Chain Areas

The Collector and Analyzer Technologies. IoT and CC produce improvements in distribution by providing customers with the proper information about the status of their orders by simply accessing the company's website. In the event that a customer detects th

The Interpreter and Transformer Technologies

Conclusions

References

Index.

Notes:

Includes bibliographical references and index.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

1-80043-876-1

OCLC:

1224368122