Data Science and Data Analytics
Data Science and Data Analytics

Author: Amit Kumar Tyagi

Publisher: CRC Press

Published: 2021-09-22

Total Pages: 483

ISBN-13: 1000423190

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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.

Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance
Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance

Author: El Bachir Boukherouaa

Publisher: International Monetary Fund

Published: 2021-10-22

Total Pages: 35

ISBN-13: 1589063953

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This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications

Author: Gary D. Miner

Publisher: Academic Press

Published: 2012-01-25

Total Pages: 1095

ISBN-13: 0123870119

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Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. - Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible - Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com - Glossary of text mining terms provided in the appendix

Applying Predictive Analytics Within the Service Sector
Applying Predictive Analytics Within the Service Sector

Author: Sahu, Rajendra

Publisher: IGI Global

Published: 2017-02-07

Total Pages: 313

ISBN-13: 1522521496

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Value creation is a prime concern for any contemporary business. This can be accomplished through the incorporation of various techniques and processes, such as the integration of analytics to improve business functions. Applying Predictive Analytics Within the Service Sector is a pivotal reference source for the latest innovative perspectives on the incorporation of analysis techniques to enhance business performance. Examining a wide range of relevant topics, such as alternative clustering, recommender systems, and social media tools, this book is ideally designed for researchers, academics, students, professionals, and practitioners seeking scholarly material on business improvement in the service industry.

Intersectional Analysis as a Method to Analyze Popular Culture
Intersectional Analysis as a Method to Analyze Popular Culture

Author: Erica B. Edwards

Publisher: Routledge

Published: 2019-11-27

Total Pages: 275

ISBN-13: 0429557000

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Intersectional Analysis as a Method to Analyze Popular Culture: Clarity in the Matrix explores how race, class, gender, sexuality, and other social categories are represented in, and constructed by, some of the most significant popular culture artifacts in contemporary Western culture. Through readings of racialized television sitcoms, LGBTQ+ representation in mainstream American music, the role of Black Panther in Western imperialist projects, and self-love narratives promoted by social media influencers, it demonstrates how novice and emerging researchers can use intersectional theory as an analysis method in the field of cultural studies. The case studies presented are contextualized through a brief history of intersectional theory, a methodological rationale for its use in relation to popular culture, and a review of the ethical considerations researchers should take before, during, and after they approach popular artifacts. Intended to be a textbook for novice and emerging researchers across a wide range of social science disciplines, this book serves as a practical guide to uncover the multiple and interlocking ways oppression is reified, resisted and/or negotiated through popular culture. 2021 Winner of the AESA Critics’ Choice Book Award

Risk Culture in Banking
Risk Culture in Banking

Author: Alessandro Carretta

Publisher: Springer

Published: 2017-10-11

Total Pages: 453

ISBN-13: 3319575929

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This book explores risk culture in banks following the financial crisis. It analyses the role of national and institutional risk culture, market competitiveness, organisational systems and institutional practices that led to a weakening of risk culture in financial institutions leading up to the financial crisis. It addresses how to assess and measure risk culture, and analyse the impact on performance and reputation. Finally it explores the impact of regulation and a variety of tools that can be applied from the board down to promote a healthy risk culture in the governance of financial institutions internal controls and risk culture in banks.

Recent Challenges in Intelligent Information and Database Systems
Recent Challenges in Intelligent Information and Database Systems

Author: Edward Szczerbicki

Publisher: Springer Nature

Published: 2022-11-23

Total Pages: 793

ISBN-13: 9811982341

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This book constitutes the refereed proceedings of the 14th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2022, held in Ho Chi Minh City, Vietnam, in November 2022. ​This volume contains 60 peer-reviewed papers selected for poster presentation from 406 submissions. Papers included in this volume cover the following topics: data mining and machine learning methods, advanced data mining techniques and applications, intelligent and contextual systems, natural language processing, network systems and applications, computational imaging and vision, decision support and control systems, and data modeling and processing for industry 4.0.

SAS Text Analytics for Business Applications
SAS Text Analytics for Business Applications

Author: Teresa Jade

Publisher: SAS Institute

Published: 2019-03-29

Total Pages: 275

ISBN-13: 1635266610

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Extract actionable insights from text and unstructured data. Information extraction is the task of automatically extracting structured information from unstructured or semi-structured text. SAS Text Analytics for Business Applications: Concept Rules for Information Extraction Models focuses on this key element of natural language processing (NLP) and provides real-world guidance on the effective application of text analytics. Using scenarios and data based on business cases across many different domains and industries, the book includes many helpful tips and best practices from SAS text analytics experts to ensure fast, valuable insight from your textual data. Written for a broad audience of beginning, intermediate, and advanced users of SAS text analytics products, including SAS Visual Text Analytics, SAS Contextual Analysis, and SAS Enterprise Content Categorization, this book provides a solid technical reference. You will learn the SAS information extraction toolkit, broaden your knowledge of rule-based methods, and answer new business questions. As your practical experience grows, this book will serve as a reference to deepen your expertise.

Text Analytics with Python
Text Analytics with Python

Author: Dipanjan Sarkar

Publisher: Apress

Published: 2016-11-30

Total Pages: 397

ISBN-13: 1484223888

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Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. What You Will Learn: Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern Who This Book Is For : IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data