Advances in Data Science and Analytics (eBook, PDF)

Concepts and Paradigms
Redaktion: Niranjanamurthy, M.; Gandomi, Amir H.; Gianey, Hemant Kumar

• Format: PDF

Produktbeschreibung

ADVANCES in DATA SCIENCE and ANALYTICS Presenting the concepts and advances of data science and analytics, this volume, written and edited by a global team of experts, also goes into the practical applications that can be utilized across multiple disciplines and industries, for both the engineer and the student, focusing on machining learning, big data, business intelligence, and analytics. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning, and big data. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries. For the purposes of this volume, data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. Although data mining and other related areas have been around for a few decades, data science and analytics are still quickly evolving, and the processes and technologies change, almost on a day-to-day basis. This volume provides an overview of some of the most important advances in these areas today, including practical coverage of the daily applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in these areas, this is a must-have for any library.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 352
• Erscheinungstermin: 24. Oktober 2022
• Englisch
• ISBN-13: 9781119792819
• Artikelnr.: 66270790

Autorenporträt

M. Niranjanamurthy, PhD, is an assistant professor in the Department of Computer Applications, M. S. Ramaiah Institute of Technology, Bangalore, Karnataka, India. He earned his PhD in computer science at JJTU. He has over 13 years of teaching experience and two years of industry experience as a software engineer. He has published four books and 85 papers in technical journals and conferences. He has six patents to his credit and has won numerous awards. Hemant Kumar Gianey, PhD, is a senior assistant professor in the Computer Science Department at Vellore Institute of Technology, AP, India. He also worked at Thapar Institute of Engineering and Technology, Patiala, Punjab, India and worked as a post-doctoral researcher in the Computer Science and Engineering Department at National Cheng Kung University in Taiwan. He has over 15 years of teaching and industry experience. He has conducted many workshops and has been a guest speaker in various universities. He has also published many research papers on in scientific and technical journals. Amir H. Gandomi, PhD, is a professor of data science in the Department of Engineering and Information Technology, University of Technology Sydney. Before joining UTS, he was an assistant professor at the School of Business, Stevens Institute of Technology, NJ, and a distinguished research fellow at BEACON Center, Michigan State University. He has published over 150 journal papers and four books and collectively has been cited more than 14,000 times. He has been named as one of the world's most influential scientific minds and a Highly Cited Researcher (top 1%) for three consecutive years, from 2017 to 2019. He has also served as associate editor, editor, and guest editor in several prestigious journals and has delivered several keynote talks. He is also part of a NASA technology cluster on Big Data, Artificial Intelligence, and Machine Learning.

Inhaltsangabe

Preface xv

1 Implementation Tools for Generating Statistical Consequence Using Data Visualization Techniques 1
Dr. Ajay B. Gadicha, Dr. Vijay B. Gadicha, Prof. Sneha Bohra and Dr. Niranjanamurthy M.

1.1 Introduction 2

1.2 Literature Review 4

1.3 Tools in Data Visualization 4

1.4 Methodology 14

1.4.1 Plotting the Data 14

1.4.2 Plotting the Model on Data 15

1.4.3 Quantifying Linear Relationships 16

1.4.4 Covariance vs. Correlation 17

1.5 Conclusion 18

References 18

2 Decision Making and Predictive Analysis for Real Time Data 21
Umesh Pratap Singh

2.1 Introduction 22

2.2 Data Analytics 23

2.2.1 Descriptive Analytics 23

2.2.2 Diagnostic Analytics 23

2.2.3 Predictive Analytics 23

2.2.4 Prescriptive Analytics 24

2.3 Predictive Modeling 24

2.4 Categories of Predictive Models 24

2.5 Process of Predictive Modeling 25

2.5.1 Requirement Gathering 26

2.5.2 Data Gathering 26

2.5.3 Data Analysis and Massaging 26

2.5.4 Machine Learning Statistics 26

2.5.5 Predictive Modeling 26

2.5.6 Prediction and Decision Making 27

2.6 Predictive Analytics Opportunities 27

2.6.1 Detecting Fraud 27

2.6.2 Reduction of Risk 27

2.6.3 Marketing Campaign Optimization 28

2.6.4 Operation Improvement 28

2.6.5 Clinical Decision Support System 28

2.7 Classification of Predictive Analytics Models 28

2.7.1 Predictive Models 28

2.7.2 Descriptive Models 29

2.7.3 Decision Models 29

2.8 Predictive Analytics Techniques 29

2.8.1 Predictive Analytics Software 29

2.8.2 The Importance of Good Data 30

2.8.3 Predictive Analytics vs. Business Intelligence 30

2.8.4 Pricing Information 30

2.9 Data Analysis Tools 30

2.9.1 Excel 30

2.9.2 Tableau 31

2.9.3 Power BI 31

2.9.4 Fine Report 31

2.9.5 R & Python 31

2.10 Advantages & Disadvantages of Predictive Modeling 31

2.10.1 Advantages 31

2.10.2 Disadvantages 32

2.10.2.1 Data Labeling 32

2.10.2.2 Obtaining Massive Training Datasets 32

2.10.2.3 The Explainability Problem 32

2.10.2.4 Generalizability of Learning 33

2.10.2.5 Bias in Algorithms and Data 33

2.11 Predictive Analytics Biggest Impact 33

2.11.1 Predicting Demand 33

2.11.2 Transformation Using Technology and Process 34

2.11.3 Improved Pricing 34

2.11.4 Predictive Maintenance 35

2.12 Application of Predictive Analytics 35

2.12.1 Financial and Banking Services 35

2.12.2 Retail 35

2.12.3 Health and Insurance 36

2.12.4 Oil and Gas Utilities 36

2.12.5 Public Sector 36

2.13 Future Scope of Predictive Modeling 36

2.13.1 Technological Advancements 37

2.13.2 Changes in Work 37

2.13.3 Risk Mitigation 37

2.14 Conclusion 37

References 38

3 Optimizing Water Quality with Data Analytics and Machine Learning 39
Bin Liang, Zhidong Li, Hongda Tian, Shuming Liang, Yang Wang and Fang Chen

3.1 Introduction 39

3.2 Related Work 41

3.3 Data Sources and Collection 42

3.4 Water Demand Forecasting 43

3.4.1 Network Flow and Zone Demand Estimation 43

3.4.2 Demand Forecasting 44

3.4.2.1 Feature Importance 45

3.4.2.2 Forecast Horizon 46

3.4.3 Per