Data Science and Data Analytics

Opportunities and Challenges

Amit Kumar Tyagi

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English

Chapman & Hall/CRC
04 October 2024

Databases; Data capture & analysis; Data mining; Computer science

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues.

Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy.

FEATURES

Gives the concept of data science, tools, and algorithms that exist for many useful applications

Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems

Identifies many areas and uses of data science in the smart era

Applies data science to agriculture, healthcare, graph mining, education, security, etc.

Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm’s productivity.

Edited by: Amit Kumar Tyagi
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions: Height: 254mm, Width: 178mm,
Weight: 453g
ISBN: 9780367628888
ISBN 10: 0367628880
Pages: 464
Publication Date: 04 October 2024
Audience: College/higher education , Professional and scholarly , Primary , Undergraduate
Format: Paperback
Publisher's Status: Active

Amit Kumar Tyagi is Assistant Professor (Senior Grade), and Senior Researcher at Vellore Institute of Technology (VIT), Chennai Campus, India. He earned his PhoD. in 2018 from Pondicherry Central University, India. He joined the Lord Krishna College of Engineering, Ghaziabad (LKCE) from 2009-2010, and 2012-2013. He was an Assistant Professor and Head - Research, Lingaya’s Vidyapeeth (formerly known as Lingaya’s University), Faridabad, Haryana, India in 2018-2019. His current research focuses on Machine Learning with Big data, Blockchain Technology, Data Science, Cyber Physical Systems, Smart and Secure Computing and Privacy. He has contributed to several projects such as ""AARIN"" and ""P3- Block"" to address some of the open issues related to the privacy breaches in Vehicular Applications (such as Parking) and Medical Cyber Physical Systems (MCPS). He has published more than 8 patents in the area of Deep Learning, Internet of Things, Cyber Physical Systems and Computer Vision. He was recently awarded best paper award for paper titled ""A Novel Feature Extractor Based on the Modified Approach of Histogram of oriented Gradient"", ICCSA 2020, Italy (Europe). He is a regular member of the ACM, IEEE, MIRLabs, Ramanujan Mathematical Society, Cryptology Research Society, and Universal Scientific Education and Research Network, CSI and ISTE.