Data Science In Theory And Practice
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Data Science In Theory And Practice
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Author : Jaydip Sen
language : en
Publisher: Cambridge Scholars Publishing
Release Date : 2024-09-13
Data Science In Theory And Practice written by Jaydip Sen and has been published by Cambridge Scholars Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-13 with Computers categories.
This comprehensive edited volume showcases the latest breakthroughs and innovative research in the rapidly evolving field of data science, and brings together contributions from leading experts and researchers who push the boundaries of the field, offering readers a deep insight into the diverse facets of this transformative discipline. Spanning a wide spectrum of topics, the chapters in this volume cover key areas such as machine learning, artificial intelligence, statistical analysis, and ethical considerations in data science. Each chapter is a testament to the ongoing quest for knowledge and the relentless pursuit of excellence in harnessing the power of data for meaningful insights and actionable intelligence. Whether you're an experienced data scientist, a researcher exploring the frontiers of the field, or a novice eager to grasp the fundamentals, this edited volume serves as a valuable resource. The compilation not only highlights the current state of data science but also anticipates future trends, paving the way for continued advancements and paradigm shifts in the way we approach, analyze, and leverage data.
Data Science In Theory And Practice
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Author : Maria C. Mariani
language : en
Publisher:
Release Date : 2021-02-17
Data Science In Theory And Practice written by Maria C. Mariani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-17 with Mathematics categories.
"This textbook is dedicated to practitioners, graduate, and advanced undergraduate students who have interest in Data Science, Business analytics, and Statistical and Mathematical Modeling in different disciplines such as Finance, Geophysics, and Engineering. This book is designed to serve as a textbook for several courses in the aforementioned areas and a reference guide for practitioners in the industry. The book has a strong theoretical background and several applications to specific practical problems. It contains numerous techniques applicable to modern data science and other disciplines. In today's world, many fields are confronted with increasingly large amounts of complex data. Financial, healthcare, and geophysical data sampled with high frequency is no exception. These staggering amounts of data pose special challenges to the world of finance and other disciplines such as healthcare and geophysics, as traditional models and information technology tools can be poorly suited to grapple with their size and complexity. Probabilistic modeling, mathematical modeling, and statistical data analysis attempt to discover order from apparent disorder; this textbook may serve as a guide to various new systematic approaches on how to implement these quantitative activities with complex data sets."--
Data Science
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Author : Tiffany Timbers
language : en
Publisher: CRC Press
Release Date : 2024-08-23
Data Science written by Tiffany Timbers and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-23 with Mathematics categories.
Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. It emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. Based on educational research and active learning principles, the book uses a modern approach to Python and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The text will leave readers well-prepared for data science projects. It is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates at the University of British Columbia. Key Features: Includes autograded worksheets for interactive, self-directed learning. Introduces readers to modern data analysis and workflow tools such as Jupyter notebooks and GitHub, and covers cutting-edge data analysis and manipulation Python libraries such as pandas, scikit-learn, and altair. Is designed for a broad audience of learners from all backgrounds and disciplines.
Fundamentals Of Data Science
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Author : Jugal K. Kalita
language : en
Publisher: Elsevier
Release Date : 2023-11-17
Fundamentals Of Data Science written by Jugal K. Kalita and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-17 with Mathematics categories.
Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining and analytics. Users will find the authors' research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data. The book's authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. In addition, a number of descriptive learning tasks are included. - Presents the foundational concepts of data science along with advanced concepts and real-life applications for applied learning - Includes coverage of a number of key topics such as data quality and pre-processing, proximity and validation, predictive data science, descriptive data science, ensemble learning, association rule mining, Big Data analytics, as well as incremental and distributed learning - Provides updates on key applications of data science techniques in areas such as Computational Biology, Network Intrusion Detection, Natural Language Processing, Software Clone Detection, Financial Data Analysis, and Scientific Time Series Data Analysis - Covers computer program code for implementing descriptive and predictive algorithms
Introduction To Data Science
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Author : Rafael A. Irizarry
language : en
Publisher: CRC Press
Release Date : 2024-08-02
Introduction To Data Science written by Rafael A. Irizarry and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-02 with Mathematics categories.
Unlike the first edition, the new edition has been split into two books. Thoroughly revised and updated, this is the first book of the second edition of Introduction to Data Science: Data Wrangling and Visualization with R. It introduces skills that can help you tackle real-world data analysis challenges. These include R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with Quarto and knitr. The new edition includes additional material/chapters on data.table, locales, and accessing data through APIs. The book is divided into four parts: R, Data Visualization, Data Wrangling, and Productivity Tools. Each part has several chapters meant to be presented as one lecture and includes dozens of exercises. The second book will cover topics including probability, statistics and prediction algorithms with R. Throughout the book, we use motivating case studies. In each case study, we try to realistically mimic a data scientist’s experience. For each of the skills covered, we start by asking specific questions and answer these through data analysis. Examples of the case studies included in the book are: US murder rates by state, self-reported student heights, trends in world health and economics, and the impact of vaccines on infectious disease rates. This book is meant to be a textbook for a first course in Data Science. No previous knowledge of R is necessary, although some experience with programming may be helpful. To be a successful data analyst implementing these skills covered in this book requires understanding advanced statistical concepts, such as those covered the second book. If you read and understand all the chapters and complete all the exercises in this book, and understand statistical concepts, you will be well-positioned to perform basic data analysis tasks and you will be prepared to learn the more advanced concepts and skills needed to become an expert.
Data Science In Theory And Practice
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Author : Maria Cristina Mariani
language : en
Publisher: John Wiley & Sons
Release Date : 2021-10-12
Data Science In Theory And Practice written by Maria Cristina Mariani and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-12 with Mathematics categories.
DATA SCIENCE IN THEORY AND PRACTICE EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.
Trends Of Data Science And Applications
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Author : Siddharth Swarup Rautaray
language : en
Publisher: Springer Nature
Release Date : 2021-03-21
Trends Of Data Science And Applications written by Siddharth Swarup Rautaray and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-21 with Computers categories.
This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021. The book covers contributions ranging from theoretical and foundation research, platforms, methods, applications, and tools in all areas. It provides theory and practices in the area of data science, which add a social, geographical, and temporal dimension to data science research. It also includes application-oriented papers that prepare and use data in discovery research. This book contains chapters from academia as well as practitioners on big data technologies, artificial intelligence, machine learning, deep learning, data representation and visualization, business analytics, healthcare analytics, bioinformatics, etc. This book is helpful for the students, practitioners, researchers as well as industry professional.
Devops For Data Science
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Author : Alex Gold
language : en
Publisher: CRC Press
Release Date : 2024-06-19
Devops For Data Science written by Alex Gold and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-19 with Business & Economics categories.
Data Scientists are experts at analyzing, modelling and visualizing data but, at one point or another, have all encountered difficulties in collaborating with or delivering their work to the people and systems that matter. Born out of the agile software movement, DevOps is a set of practices, principles and tools that help software engineers reliably deploy work to production. This book takes the lessons of DevOps and aplies them to creating and delivering production-grade data science projects in Python and R. This book’s first section explores how to build data science projects that deploy to production with no frills or fuss. Its second section covers the rudiments of administering a server, including Linux, application, and network administration before concluding with a demystification of the concerns of enterprise IT/Administration in its final section, making it possible for data scientists to communicate and collaborate with their organization’s security, networking, and administration teams. Key Features: • Start-to-finish labs take readers through creating projects that meet DevOps best practices and creating a server-based environment to work on and deploy them. • Provides an appendix of cheatsheets so that readers will never be without the reference they need to remember a Git, Docker, or Command Line command. • Distills what a data scientist needs to know about Docker, APIs, CI/CD, Linux, DNS, SSL, HTTP, Auth, and more. • Written specifically to address the concern of a data scientist who wants to take their Python or R work to production. There are countless books on creating data science work that is correct. This book, on the otherhand, aims to go beyond this, targeted at data scientists who want their work to be than merely accurate and deliver work that matters.
Data Science Thinking
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Author : Longbing Cao
language : en
Publisher: Springer
Release Date : 2018-08-17
Data Science Thinking written by Longbing Cao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-17 with Computers categories.
This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.
Models Demystified
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Author : Michael Clark
language : en
Publisher: CRC Press
Release Date : 2025-08-15
Models Demystified written by Michael Clark and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-15 with Mathematics categories.
Unlock the Power of Data Science and Machine Learning In this comprehensive guide, we delve into the world of data science, machine learning, and AI modeling, providing readers with a robust foundation and practical skills to tackle real-world problems. From basic modeling techniques to advanced machine learning algorithms, this book covers a wide range of topics,ensuring that readers at all levels can benefit from its content. Each chapter is meticulously crafted to offer clear explanations, hands-on examples, and code snippets in both Python and R, making complex concepts accessible and actionable. Additional focus is placed on model interpretation and estimation, common data issues, modeling pitfalls to avoid, and best practices for modeling in general.