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What is Data Science

Chapter 78
Encyclopedia of Organizational Knowledge, Administration, and Technology
Data science is a multi-disciplinary field that follows scientific approaches, methods, and processes to extract knowledge and insights from structured, semi-structured and unstructured data.
Published in Chapter:
Integrating Big Data Technology Into Organizational Decision Support Systems
Ahmad M. Kabil (University of Wisconsin-Whitewater, USA)
Copyright: © 2021 |Pages: 18
DOI: 10.4018/978-1-7998-3473-1.ch078
Abstract
With the growing trend of global competitiveness and business complexity, the need arises for integrating BDTs in the design of ODSS. BDTs enable ODSS to deal with data of volume, variety, and velocity beyond the capability of traditional databases and data-warehouses in ODSS. The basic modules of updated ODSS with BDT are presented as a conceptual design, which provides a general foundation for the detailed design and implementation of an application. The validation of the proposed conceptual design is conducted on a test-case based upon the BSC perspectives as well as requirements common for ODSS scenarios. The results show that each requirement of the system is supported by a set of integrated modules in a balanced way. The modules of the highest contribution in the conceptual design are the modules most related to BDT. The initial stages of the implementation of the system are given.
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Chapter 3
The Effects of Labor Markets and Trade Openness on Economic Growth: A Panel Data Analysis for G-20 C
An interdisciplinary field involving processes, theories, concepts, tools, and technologies, that enable the review, analysis, and extraction of valuable knowledge and information from structured and unstructured (raw) data.
Published in Chapter: The Effects of Labor Markets and Trade Openness on Economic Growth: A Panel Data Analysis for G-20 C; From: Reskilling the Workforce in the Labor Market: The Country Cases
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Chapter 25
Meet Industry Needs in the Big Data Era: Data Science Curricula Development
A branch of computer science that utilizes mathematical analytical methods to study big data in order to retrieve useful information.
Published in Chapter: Meet Industry Needs in the Big Data Era: Data Science Curricula Development; From: Data-Driven Intelligent Business Sustainability
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Chapter 1
Introduction to Data Science
Data Science is the science and art of using computational methods to identify and discover influential patterns in data.
Published in Chapter: Introduction to Data Science; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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Chapter 6
Orienting Data Services in the Library: Evolution of a Digital Scholarship Services Department
An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge from data and to apply that knowledge across a broad range of domains.
Published in Chapter: Orienting Data Services in the Library: Evolution of a Digital Scholarship Services Department; From: Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems
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Chapter 3
Big Data Applications in Business
It is a new discipline that combines elements of mathematics, statistics, computer science, and data visualization. The objective is to extract information from data sources. In this sense, data science is devoted to database exploration and analysis. This discipline has recently received much attention due to the growing interest in big data.
Published in Chapter: Big Data Applications in Business; From: Big Data Analytics for Entrepreneurial Success
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Chapter 16
Pestel Analysis and the Porter's Five Forces: An Integrated Model of Strategic Sectors
The use of statistics and artificial intelligence to handle large amounts of data for advanced analysis.
Published in Chapter: Pestel Analysis and the Porter's Five Forces: An Integrated Model of Strategic Sectors; From: Handbook of Research on Organizational Sustainability in Turbulent Economies
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Chapter 17
Data Science and Big Data Analytics in Financial Services: A Case Study
The extraction of knowledge from large volumes of unstructured data which is a continuation of the field data mining and predictive analytics, also known as knowledge discovery and data mining (KDD).
Published in Chapter: Data Science and Big Data Analytics in Financial Services: A Case Study; From: Handbook of Research on Driving Competitive Advantage through Sustainable, Lean, and Disruptive Innovation
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Chapter 23
A Meta-Analytical Review of Deep Learning Prediction Models for Big Data
Data science is word derived from data and science which include statistics, scientific methods, artificial intelligence, data analysis in order to extract information from data.
Published in Chapter: A Meta-Analytical Review of Deep Learning Prediction Models for Big Data; From: Encyclopedia of Data Science and Machine Learning
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Chapter 6
Data Science Tools Application for Business Processes Modelling in Aviation
Is a broad field that refers to the collective processes, theories, concepts, tools and technologies that enable the review, analysis, and extraction of valuable knowledge and information from raw data. It is geared toward helping individuals and organizations make better decisions from stored, consumed and managed data.
Published in Chapter: Data Science Tools Application for Business Processes Modelling in Aviation; From: Cases on Modern Computer Systems in Aviation
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Chapter 25
Toward the 4th Agenda 2030 Goal: AI Support to Executive Functions for Inclusions
Drawing from statistics, computer science and information science, this interdisciplinary field aims to use a variety of scientific methods, processes, and systems to solve problems involving data.
Published in Chapter: Toward the 4th Agenda 2030 Goal: AI Support to Executive Functions for Inclusions; From: Handbook of Research on Teaching With Virtual Environments and AI
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Chapter 10
Big Data Analytics for Smart Airport Management
Is a field that builds on and synthesizes a number of relevant disciplines and bodies of knowledge, including statistics, informatics, computing, communication, management, and sociology to translate data into information, knowledge, insight, and intelligence for improving innovation, productivity and decision making.
Published in Chapter: Big Data Analytics for Smart Airport Management; From: Intelligent Analytics With Advanced Multi-Industry Applications
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Chapter 5
Understanding Data Analytics Is Good but Knowing How to Use It Is Better!
It is a new discipline that combines elements of mathematics, statistics, computer science, and data visualization. The objective is to extract information from data sources. In this sense, data science is devoted to database exploration and analysis. This discipline has recently received much attention due to the growing interest in big data.
Published in Chapter: Understanding Data Analytics Is Good but Knowing How to Use It Is Better!; From: Big Data Analytics for Entrepreneurial Success
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Chapter 21
A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics: Healthcare Applications
Data Science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights.
Published in Chapter: A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics: Healthcare Applications; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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Chapter 13
Electricity Production Prediction by Microsoft Azure Machine Learning Service and Python User Blocks
Is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.
Published in Chapter: Electricity Production Prediction by Microsoft Azure Machine Learning Service and Python User Blocks; From: Machine Learning and Computer Vision for Renewable Energy
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Chapter 3
A Survey on Tools for Data Analytics and Data Science
Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data—both structured and unstructured.
Published in Chapter: A Survey on Tools for Data Analytics and Data Science; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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Chapter 9
Security Risks of Biomedical Data Processing in Cloud Computing Environment
A knowledge acquisition from data through scientific method that comprises systematic observation, experiment, measurement, formulation, and hypotheses testing with the aim of discovering new ideas and concepts.
Published in Chapter: Security Risks of Biomedical Data Processing in Cloud Computing Environment; From: Handbook of Research on Information Security in Biomedical Signal Processing
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Chapter 4
Demand Forecasting Models With Time Series and Random Forest
An interdisciplinary field of study of data in relation to mathematics, statistics, operations research, data analysis, data mining, machine learning, computer science, engineering, visualization, data privacy, and big data.
Published in Chapter: Demand Forecasting Models With Time Series and Random Forest; From: Driving Innovation and Productivity Through Sustainable Automation
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Chapter 2
Interdisciplinary Application of Machine Learning, Data Science, and Python for Cricket Analytics
A multidisciplinary field that uses statistical and computational methods to extract knowledge and insights from data.
Published in Chapter: Interdisciplinary Application of Machine Learning, Data Science, and Python for Cricket Analytics; From: Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science
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Chapter 18
The Evolution of AI and Data Science
Data Science is an interdisciplinary field that focuses on sourcing, managing, preparing, and analyzing raw data to extract meaningful insights that help find solutions to problems and in decision-making.
Published in Chapter: The Evolution of AI and Data Science; From: The Ethical Frontier of AI and Data Analysis
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Chapter 10
Applying Intelligent Big Data Analytics in a Smart Airport Business: Value, Adoption, and Challenges
Is a field that builds on and synthesizes a number of relevant disciplines and bodies of knowledge, including statistics, informatics, computing, communication, management, and sociology to translate data into information, knowledge, insight, and intelligence for improving innovation, productivity and decision making.
Published in Chapter: Applying Intelligent Big Data Analytics in a Smart Airport Business: Value, Adoption, and Challenges; From: Handbook of Research on Foundations and Applications of Intelligent Business Analytics
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Chapter 3
A Systematic Bibliometric Literature Review on Data Science in Marketing
Is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured data.
Published in Chapter: A Systematic Bibliometric Literature Review on Data Science in Marketing; From: Enhancing Business Communications and Collaboration Through Data Science Applications
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Chapter 49
Designing a Business Analytics Culture in Organizations in India
With the increase in the amount of data captured by organizations, the various forms of structured and unstructured captured and the speed and frequency of it, significance of data science has increased over time. Data science is a methodical form of integrating statistics, algorithms, scientific methods, models and visualization methods for interpretation of outcomes in organizational problem solving and fact based decision making.
Published in Chapter: Designing a Business Analytics Culture in Organizations in India; From: Encyclopedia of Organizational Knowledge, Administration, and Technology
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Chapter 99
Emerging Tools and Technologies in Data Science
The field of knowledge dealing with the total processing viz. collection, classification, comprehension, interpretation, etc., of raw facts in any discipline to convert those into the required information to be used by different government, non-government, non-profit, profit or business organizations working in or involved with the corresponding discipline, to make informative decision on the basis of authentic data. Using this sorts of data supports to make data based decisions is known as Decision Support System (DSS)
Published in Chapter: Emerging Tools and Technologies in Data Science; From: Encyclopedia of Data Science and Machine Learning
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Chapter 14
Ethical Risks in the Cross Section of Extended Reality (XR), Geographic Information Systems (GIS), and Artificial Intelligence (AI)
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
Published in Chapter: Ethical Risks in the Cross Section of Extended Reality (XR), Geographic Information Systems (GIS), and Artificial Intelligence (AI); From: Applied Ethics in a Digital World
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Chapter 25
The Contribution of Data Science Applied to Customer Relationship Management: A Systematic Literature Review
Recent popular field related to business analytics research and its intelligent techniques applied to big data.
Published in Chapter: The Contribution of Data Science Applied to Customer Relationship Management: A Systematic Literature Review; From: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
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Chapter 12
Machine Learning and Its Application in Monitoring Diabetes Mellitus
It is a science of multiple disciplines used for exploring knowledge from data using complex scientific algorithms and methods.
Published in Chapter: Machine Learning and Its Application in Monitoring Diabetes Mellitus; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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Chapter 184
Improving Transportation Planning Using Machine Learning
Is the art of using to tell a story from the data, involves advanced mathematical and statistical techniques that solve complex problems.
Published in Chapter: Improving Transportation Planning Using Machine Learning; From: Encyclopedia of Data Science and Machine Learning
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Chapter 11
Applying Sentiment Analysis Techniques in Social Media Data About Threat of Armed Conflicts Using Two Times Series Models
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Chapter 212
Supply Chain Analytics: Challenges and Opportunities
A set of quantitative and qualitative methods that support and guide the extraction of information and knowledge from data to solve relevant problems and predict outcomes.
Published in Chapter: Supply Chain Analytics: Challenges and Opportunities; From: Encyclopedia of Business Analytics and Optimization
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Chapter 14
Artificial Intelligence a Driver for Digital Transformation
Composed by mathematics, statistics, probability, computing, and data visualization to extract knowledge from a heterogeneous set of data (images, sound, text, genomic data, social network links, physical measurements, and others sources).
Published in Chapter: Artificial Intelligence a Driver for Digital Transformation; From: Handbook of Research on Digital Transformation and Challenges to Data Security and Privacy
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Chapter 20
Library Engagement With Emerging Technologies in Research and Learning
A core competency for researchers, students, and scholars in many or most disciplines that routinely rely on computational data analysis in their research and learning.
Published in Chapter: Library Engagement With Emerging Technologies in Research and Learning; From: Handbook of Research on Facilitating Collaborative Learning Through Digital Content and Learning Technologies
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Chapter 5
Machine Learning-Enabled Internet of Things Solution for Smart Agriculture Operations
Technology is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms, and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data.
Published in Chapter: Machine Learning-Enabled Internet of Things Solution for Smart Agriculture Operations; From: Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries
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Chapter 1
Video Call-Based Real-Time Crime Reporting System
A field related to extracting and analyzing of data to derive useful insights and inferences from it.
Published in Chapter: Video Call-Based Real-Time Crime Reporting System; From: Designing User Interfaces With a Data Science Approach
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Chapter 5
Ethical Navigations: Adaptable Frameworks for Responsible AI Use in Higher Education
An interdisciplinary field of technology that uses algorithms and processes to gather and analyze large amounts of data to uncover patterns and insights that inform business decisions.
Published in Chapter: Ethical Navigations: Adaptable Frameworks for Responsible AI Use in Higher Education; From: Exploring the Ethical Implications of Generative AI
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Chapter 166
Data Science and Distributed Intelligence
A collection of models, techniques and algorithms that focus on the issues of gathering, pre-processing, and making sense-out of large repositories of data, which are seen as “data products.”
Published in Chapter: Data Science and Distributed Intelligence; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 10
A Web-Based Platform for Crop-Specific Data Management and Exchange of Farmers' Experiences
It is a general method that combines theories and techniques from databases, mathematics, statistics, artificial intelligence, and information visualization, with the purpose of analyzing data to obtain patterns that generate insights and novel knowledge.
Published in Chapter: A Web-Based Platform for Crop-Specific Data Management and Exchange of Farmers' Experiences; From: Precision Agriculture Technologies for Food Security and Sustainability
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Chapter 1
The Nature of Intelligent Analytics
Is a field that builds on and synthesizes a number of relevant disciplines and bodies of knowledge, including statistics, informatics, computing, communication, management, and sociology to translate data into information, knowledge, insight and intelligence for improving innovation, productivity and decision making.
Published in Chapter: The Nature of Intelligent Analytics; From: Intelligent Analytics With Advanced Multi-Industry Applications
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Chapter 12
Threat Emotion Analysis in Social Media: Considering Armed Conflicts as Social Extreme Events
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Chapter 70
Data Science Methodology
Data science is the higher-level process of developing an analytical objective, providing, and preparing data, conducting the analysis and utilization, or disseminating the results.
Published in Chapter: Data Science Methodology; From: Encyclopedia of Data Science and Machine Learning
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Chapter 1
Intelligent Big Data Analytics: A Managerial Perspective
A field that builds on and synthesizes a number of relevant disciplines and bodies of knowledge, including statistics, informatics, computing, communication, management, and sociology to translate data into information, knowledge, insight, and intelligence for improving innovation, productivity, and decision making.
Published in Chapter: Intelligent Big Data Analytics: A Managerial Perspective; From: Managerial Perspectives on Intelligent Big Data Analytics
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Chapter 12
Undergraduate Computer Science Capstone Projects: Experiences and Examples in Data Science
Is emerging research disciplinary area that relates to big data, machine learning, and cloud computing.
Published in Chapter: Undergraduate Computer Science Capstone Projects: Experiences and Examples in Data Science; From: Handbook of Research on Foundations and Applications of Intelligent Business Analytics
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Chapter 169
Big Data Analytics in Supply Chain Management
The concept that utilizes scientific and software methods, IT infrastructure, processes, and software systems in order to gather, process, analyze and deliver useful information, knowledge and insights from various data sources.
Published in Chapter: Big Data Analytics in Supply Chain Management; From: Encyclopedia of Organizational Knowledge, Administration, and Technology
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Chapter 5
Using Online Digital Data to Infer Valuable Skills for the Modern Workforce
An interdisciplinary field whose objective is to extract and interpret knowledge and insights using scientific methods, processes, algorithms, and systems.
Published in Chapter: Using Online Digital Data to Infer Valuable Skills for the Modern Workforce; From: Handbook of Research on New Media, Training, and Skill Development for the Modern Workforce
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Chapter 15
Data Analysis on Global Stratification
Data Science is the branch of science that uses technologies to predict the upcoming nature of different things such as a market or weather conditions. It shows a wide usage in today’s world.
Published in Chapter: Data Analysis on Global Stratification; From: Multi-Criteria Decision Analysis in Management
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Chapter 11
Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection
This is an evolving field that deals with the gathering, preparation, exploration, visualization, organisation, and storage of large groups of data and the extraction of valuable information from large volumes of data that may exist in an unorganised or unstructured form.
Published in Chapter: Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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Chapter 7
BIG Data: An Enabler in Developing Business Models in Cloud Computing Environments
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured similar to data mining.
Published in Chapter: BIG Data: An Enabler in Developing Business Models in Cloud Computing Environments; From: Global Virtual Enterprises in Cloud Computing Environments
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Chapter 1
Educational Data Mining: A Guide for Educational Researchers
A relatively new term applied to an interdisciplinary field of study focused on methods for collecting, maintaining, processing, analyzing and presenting results from large datasets.
Published in Chapter: Educational Data Mining: A Guide for Educational Researchers; From: Advancing Educational Research With Emerging Technology
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