Applied Data Science

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Applied Data Science

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Author : Martin Braschler
language : en
Publisher: Springer
Release Date : 2019-06-13

Applied Data Science written by Martin Braschler and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-13 with Computers categories.

This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science:first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.

Applied Data Science

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Author : Douglas G. Woolford
language : en
Publisher: Springer Nature
Release Date : 2023-05-09

Applied Data Science written by Douglas G. Woolford and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-09 with Computers categories.

The use of data to guide action is growing. Even the public uses data to guide everyday decisions! How do we develop data acumen across a broad range of fields and varying levels of expertise? How do we foster the development of effective data translators? This book explores these questions, presenting an interdisciplinary collection of edited contributions across fields such as education, health sciences, natural sciences, politics, economics, business and management studies, social sciences, and humanities. Authors illustrate how to use data within a discipline, including visualization and analysis, translating and communicating results, and pedagogical considerations. This book is of interest to scholars and anyone looking to understand the use of data science across disciplines. It is ideal in a course for non-data science majors exploring how data translation occurs in various contexts and for professionals looking to engage in roles requiring data translation.

Applied Data Science And Smart Systems

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Author : Jaiteg Singh
language : en
Publisher: CRC Press
Release Date : 2024-07-22

Applied Data Science And Smart Systems written by Jaiteg Singh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-22 with Computers categories.

The Second International Conference on Applied Data Science and Smart Systems (ADSSS-2023) was held on 15-16 December 2023 at Chitkara University, Punjab, India. This multidisciplinary conference focussed on innovation and progressive practices in science, technology, and management. The conference successfully brought together researchers, academicians, and practitioners across different domains such as artificial intelligence and machine learning, software engineering, automation, data science, business computing, data communication and computer networks. The presenters shared their most recent research works that are critical to contemporary business and societal landscape and encouraged the participants to devise solutions for real-world challenges. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND)] 4.0 license.

Applied Data Science And Machine Learning For Business Optimization 2025

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Author : Manish tripathi, Dr. Anshita Shukla
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Applied Data Science And Machine Learning For Business Optimization 2025 written by Manish tripathi, Dr. Anshita Shukla and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE In today’s data-driven world, businesses are increasingly turning to data science and machine learning (ML) to gain a competitive edge, optimize operations, and make informed decisions. The ability to harness large volumes of data and apply advanced analytical techniques is transforming industries, enabling businesses to improve efficiency, reduce costs, and unlock new growth opportunities. As we enter an era where data is one of the most valuable assets, understanding how to apply data science and ML to real-world business problems is becoming an essential skill for professionals across all sectors. “Applied Data Science and Machine Learning for Business Optimization” aims to provide practical insights into how data science and ML can be utilized to optimize business functions and drive strategic decision-making. This book bridges the gap between theory and practice, offering actionable guidance on implementing advanced analytics and machine learning techniques to solve common business challenges. Whether you are a business analyst, data scientist, or decision-maker, this book equips you with the tools, techniques, and real-world examples needed to leverage data science for business success. The core focus of this book is on applying data science and ML to optimize critical areas of business, such as operations, marketing, customer experience, finance, and supply chain management. Each chapter walks through the methodologies used in data analysis, model building, and performance evaluation, providing a hands-on approach that empowers readers to apply these techniques to their own business contexts. From predictive analytics to recommendation systems, natural language processing, and optimization algorithms, the book covers a wide range of ML tools that are instrumental in solving real-world business problems. A major goal of this book is to showcase the power of data-driven decision-making. With the exponential growth of data and computing power, businesses now have unprecedented opportunities to analyze trends, predict future outcomes, and automate decision-making processes. However, it’s crucial to approach these opportunities with a clear understanding of how to integrate data science and ML into the organizational workflow, while ensuring alignment with business goals and strategies. We believe that the application of data science and ML should not be limited to advanced technologists alone. This book is written to demystify these technologies and make them accessible to business professionals, regardless of their technical background. By focusing on practical case studies, real-world examples, and step-by-step instructions, we hope to empower readers to implement data science and ML solutions that drive measurable business outcomes. Ultimately, the journey of business optimization through data science and machine learning is a continual process of learning, adapting, and evolving. As businesses begin to adopt and scale these technologies, they will unlock new capabilities, enhance operational efficiencies, and build a more agile, data-driven organization. “Applied Data Science and Machine Learning for Business Optimization” serves as a foundational resource to help navigate this transformative journey. We hope this book inspires you to harness the power of data science and machine learning in your own organization, unlocking innovative solutions and driving impactful changes in your business. Authors

Applied Data Science In Tourism

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Author : Roman Egger
language : en
Publisher: Springer Nature
Release Date : 2022-01-31

Applied Data Science In Tourism written by Roman Egger and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-31 with Business & Economics categories.

Access to large data sets has led to a paradigm shift in the tourism research landscape. Big data is enabling a new form of knowledge gain, while at the same time shaking the epistemological foundations and requiring new methods and analysis approaches. It allows for interdisciplinary cooperation between computer sciences and social and economic sciences, and complements the traditional research approaches. This book provides a broad basis for the practical application of data science approaches such as machine learning, text mining, social network analysis, and many more, which are essential for interdisciplinary tourism research. Each method is presented in principle, viewed analytically, and its advantages and disadvantages are weighed up and typical fields of application are presented. The correct methodical application is presented with a "how-to" approach, together with code examples, allowing a wider reader base including researchers, practitioners, and students entering the field. The book is a very well-structured introduction to data science – not only in tourism – and its methodological foundations, accompanied by well-chosen practical cases. It underlines an important insight: data are only representations of reality, you need methodological skills and domain background to derive knowledge from them - Hannes Werthner, Vienna University of Technology Roman Egger has accomplished a difficult but necessary task: make clear how data science can practically support and foster travel and tourism research and applications. The book offers a well-taught collection of chapters giving a comprehensive and deep account of AI and data science for tourism - Francesco Ricci, Free University of Bozen-Bolzano This well-structured and easy-to-read book provides a comprehensive overview of data science in tourism. It contributes largely to the methodological repository beyond traditional methods. - Rob Law, University of Macau

Handbook Of Research On Applied Data Science And Artificial Intelligence In Business And Industry

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Author : Chkoniya, Valentina
language : en
Publisher: IGI Global
Release Date : 2021-06-25

Handbook Of Research On Applied Data Science And Artificial Intelligence In Business And Industry written by Chkoniya, Valentina and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-25 with Computers categories.

The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students.

Applied Data Science With R For Ai And Machine Learning

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Author : Dr S Alex David
language : en
Publisher: Leilani Katie Publication
Release Date : 2025-05-17

Applied Data Science With R For Ai And Machine Learning written by Dr S Alex David and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-17 with Computers categories.

Dr S Alex David, Professor and Head, Department of Artificial Intelligence and Machine Learning, Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India. Dr.Konda Hari Krishna, Associate Professor, Department of Computer Science & Engineering, School of Computing, Mohan Babu University, Tirupati, Andhra Pradesh, India. Mrs.A.Bharathi, Assistant Professor, Department of Computer Science, Audisankara College of Engineering and Technology (Autonoumous), Gudur, Tirupati, Andhra Pradesh, India. Dr Almas Begum, Professor, Department of Computer Science & Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamil Nadu, India.

Machine Learning And Knowledge Discovery In Databases Applied Data Science And Demo Track

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Author : Gianmarco De Francisci Morales
language : en
Publisher: Springer Nature
Release Date : 2023-09-16

Machine Learning And Knowledge Discovery In Databases Applied Data Science And Demo Track written by Gianmarco De Francisci Morales and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-16 with Computers categories.

The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: ​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: ​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: ​Robustness; Time Series; Transfer and Multitask Learning. Part VI: ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. ​Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Machine Learning And Knowledge Discovery In Databases Applied Data Science Track

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Author : Albert Bifet
language : en
Publisher: Springer Nature
Release Date : 2024-09-01

Machine Learning And Knowledge Discovery In Databases Applied Data Science Track written by Albert Bifet and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-01 with Computers categories.

This multi-volume set, LNAI 14941 to LNAI 14950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024. The papers presented in these proceedings are from the following three conference tracks: - Research Track: The 202 full papers presented here, from this track, were carefully reviewed and selected from 826 submissions. These papers are present in the following volumes: Part I, II, III, IV, V, VI, VII, VIII. Demo Track: The 14 papers presented here, from this track, were selected from 30 submissions. These papers are present in the following volume: Part VIII. Applied Data Science Track: The 56 full papers presented here, from this track, were carefully reviewed and selected from 224 submissions. These papers are present in the following volumes: Part IX and Part X.

Applying Data Science

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Author : Arthur K. Kordon
language : en
Publisher: Springer Nature
Release Date : 2020-09-12

Applying Data Science written by Arthur K. Kordon and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-12 with Computers categories.

This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.