Practical Statistical Learning And Data Science Methods
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Practical Statistical Learning And Data Science Methods
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Author : O. Olawale Awe
language : en
Publisher: Springer Nature
Release Date : 2024-12-27
Practical Statistical Learning And Data Science Methods written by O. Olawale Awe and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-27 with Computers categories.
This contributed volume offers practical implementation strategies for statistical learning and data science techniques, with fully peer-reviewed papers that embody insights and experiences gathered within the LISA 2020 Global Network. Through a series of compelling case studies, readers are immersed in practical methodologies, real-world applications, and innovative approaches in statistical learning and data science. Topics covered in this volume span a wide array of applications, including machine learning in health data analysis, deep learning models for precipitation modeling, interpretation techniques for machine learning models in BMI classification for obesity studies, as well as a comparative analysis of sampling methods in machine learning health applications. By addressing the evolving landscape of data analytics in many ways, this volume serves as a valuable resource for practitioners, researchers, and students alike. The LISA 2020 Global Network is dedicated to enhancing statistical and data science capabilities in developing countries through the establishment of collaboration laboratories, also known as “stat labs.” These stat labs function as engines for development, nurturing the next generation of collaborative statisticians and data scientists while providing essential research infrastructure for researchers, data producers, and decision-makers.
Handbook Of Research On Data Science For Effective Healthcare Practice And Administration
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Author : Noughabi, Elham Akhond Zadeh
language : en
Publisher: IGI Global
Release Date : 2017-07-20
Handbook Of Research On Data Science For Effective Healthcare Practice And Administration written by Noughabi, Elham Akhond Zadeh and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-20 with Computers categories.
Data science has always been an effective way of extracting knowledge and insights from information in various forms. One industry that can utilize the benefits from the advances in data science is the healthcare field. The Handbook of Research on Data Science for Effective Healthcare Practice and Administration is a critical reference source that overviews the state of data analysis as it relates to current practices in the health sciences field. Covering innovative topics such as linear programming, simulation modeling, network theory, and predictive analytics, this publication is recommended for all healthcare professionals, graduate students, engineers, and researchers that are seeking to expand their knowledge of efficient techniques for information analysis in the healthcare professions.
Sustainable Statistical And Data Science Methods And Practices
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Author : O. Olawale Awe
language : en
Publisher: Springer Nature
Release Date : 2024-01-05
Sustainable Statistical And Data Science Methods And Practices written by O. Olawale Awe and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-05 with Computers categories.
This volume gathers papers presented at the LISA 2020 Sustainability Symposium in Kumasi, Ghana, May 2–6, 2022. They focus on sustainable methods and practices of using statistics and data science to address real-world problems. From utilizing social media for statistical collaboration to predicting obesity among rural women, and from analyzing inflation in Nigeria using machine learning to teaching data science in Africa, this book explores the intersection of data, statistics, and sustainability. With practical applications, code snippets, and case studies, this book offers valuable insights for researchers, policymakers, and data enthusiasts alike. The LISA 2020 Global Network aims to enhance statistical and data science capability in developing countries through the creation of a network of collaboration laboratories (also known as “stat labs”). These stat labs are intended to serve as engines for development by training the next generation of collaborative statisticians and data scientists, providing research infrastructure for researchers, data producers, and decision-makers, and enabling evidence-based decision-making that has a positive impact on society. The research conducted at LISA 2020 focuses on practical methods and applications for sustainable growth of statistical capacity in developing nations.
Statistics And Data Analysis Essentials
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Author : Jayant Ramaswamy
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Statistics And Data Analysis Essentials written by Jayant Ramaswamy and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Science categories.
"Statistics and Data Analysis Essentials" is a comprehensive guide that helps readers master statistical concepts and their practical applications. Crafted by experts, this textbook combines clear explanations, real-world examples, and engaging exercises to enhance learning. We cover a broad spectrum of topics, including descriptive statistics, inferential statistics, regression analysis, and hypothesis testing, making each section accessible to learners of all levels. Real-life case studies from diverse fields such as economics, psychology, biology, and engineering demonstrate the relevance of statistical methods. Each chapter offers exercises from basic calculations to complex data analysis tasks, helping readers practice and solidify their skills. A detailed glossary provides clear definitions of key statistical terms, and additional resources, including datasets and software tutorials, are available to further support the learning experience. "Statistics and Data Analysis Essentials" is ideal for undergraduate and graduate students, as well as professionals and researchers looking to enhance their statistical expertise for practical applications.
Proceedings Of Ninth International Congress On Information And Communication Technology
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Author : Xin-She Yang
language : en
Publisher: Springer Nature
Release Date : 2024-07-27
Proceedings Of Ninth International Congress On Information And Communication Technology written by Xin-She Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-27 with Technology & Engineering categories.
This book gathers selected high-quality research papers presented at the Ninth International Congress on Information and Communication Technology, held in London, on February 19–22, 2024. It discusses emerging topics pertaining to information and communication technology (ICT) for managerial applications, e-governance, e-agriculture, e-education and computing technologies, the Internet of Things (IoT), and e-mining. Written by respected experts and researchers working on ICT, the book offers an asset for young researchers involved in advanced studies. The work is presented in ten volumes.
An Insight Into University Medical And Health Science Courses
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Author : Sunjoo Kang
language : en
Publisher: Frontiers Media SA
Release Date : 2022-12-26
An Insight Into University Medical And Health Science Courses written by Sunjoo Kang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-26 with Medical categories.
Big Data And Social Science
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Author : Ian Foster
language : en
Publisher: CRC Press
Release Date : 2020-11-17
Big Data And Social Science written by Ian Foster and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-17 with Mathematics categories.
Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations. Features: Takes an accessible, hands-on approach to handling new types of data in the social sciences Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes Illustrates social science and data science principles through real-world problems Links computer science concepts to practical social science research Promotes good scientific practice Provides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHub New to the Second Edition: Increased use of examples from different areas of social sciences New chapter on dealing with Bias and Fairness in Machine Learning models Expanded chapters focusing on Machine Learning and Text Analysis Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.
Statistics Data Mining And Machine Learning In Astronomy
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Author : Željko Ivezić
language : en
Publisher: Princeton University Press
Release Date : 2020
Statistics Data Mining And Machine Learning In Astronomy written by Željko Ivezić and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
"As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. The updates in this new edition will include fixing "code rot," correcting errata, and adding some new sections. In particular, the new sections include new material on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest"--
Just Enough Data Science And Machine Learning
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Author : Mark Levene
language : en
Publisher: Addison-Wesley Professional
Release Date : 2024-12-04
Just Enough Data Science And Machine Learning written by Mark Levene and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-04 with Business & Economics categories.
An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and concepts Coverage of various types of data and techniques for analysis In-depth exploration of popular machine learning tools and methods Insight into specific data science topics, such as social networks and sentiment analysis Practical examples and case studies for real-world application Recommended further reading for deeper exploration of specific topics.
Cybersecurity Data Science
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Author : Scott Mongeau
language : en
Publisher: Springer Nature
Release Date : 2021-10-01
Cybersecurity Data Science written by Scott Mongeau and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-01 with Computers categories.
This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession. This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography.