Название: Frame Theory in Data Science
Автор: Zhihuа Zhаng, Раllе Е.Т. Jоrgеnsеn
Издательство: Springer
Год: 2024
Страниц: 262
Язык: английский
Формат: pdf (true), epub
Размер: 39.3 MB

This book establishes brand-new frame theory and technical implementation in Data Science, with a special focus on spatial-scale feature extraction, network dynamics, object-oriented analysis, data-driven environmental prediction, and climate diagnosis. Given that Data Science is unanimously recognized as a core driver for achieving Sustainable Development Goals of the United Nations, these frame techniques bring fundamental changes to multi-channel data mining systems and support the development of digital Earth platforms. This book integrates the authors' frame research in the past twenty years and provides cutting-edge techniques and depth for scientists, professionals, and graduate students in Data Science, applied mathematics, environmental science, and geoscience. In this book, we have established the theory of Dirac frames, polynomial frames, quasiorthogonal frames, periodic frames, and frame trees, and characterized adaptive segmentation of data spectral domain by quasi-orthogonal and pseudo-project frame operators. As an emerging branch of statistical and Deep Learning, frame networks can automatically acquire novel knowledge from observation data through a statistical learning process and then makes reliable predictions and downscaling. A frame network consists of three layers: the input layer, the hidden layer, and the output layer, where various frames are embedded into each node of the hidden layer and frame coefficients are used as the weight of the directed edges from one node to another node.

Название: Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science
Автор: Inаm Ullаh, Inаm Ullаh Кhаn, Маriуа Оuаissа
Издательство: CRC Press
Год: 2024
Страниц: 253
Язык: английский
Формат: pdf (true)
Размер: 16.0 MB

Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science mainly focuses on the techniques of Artificial Intelligence (AI), Internet of Things (IoT) and Data Science for future communications systems. The goal of AI, IoT and data science for future communications systems is to create a venue for industry and academics to collaborate on the development of network and system solutions based on Data Science, AI and IoT. Recent breakthroughs in IoT, mobile and fixed communications and computation have paved the way for a data‑centric society of the future. New applications are increasingly reliant on machine‑to‑machine connections, resulting in unusual workloads and the need for more efficient and dependable infrastructures. Such a wide range of traffic workloads and applications will necessitate dynamic and highly adaptive network environments capable of self‑optimization for the task at hand while ensuring high dependability and ultra‑low latency. Networking devices, sensors, agents, meters and smart vehicles/systems generate massive amounts of data, necessitating new levels of security, performance and dependability. Such complications necessitate the development of new tools and approaches for providing successful services, management and operation. Data Science is basically utilized to extract meaningful information generated by Artificial Intelligence of Things (AIoT) devices. This will open new opportunities for researchers, engineers and scientists. The merger of AI, IoT and Data Science will enhance communication systems. This book provides detailed information about future communication systems. Also, it explains the relationship between AI, IoT and Data Science.

Название: From Concepts to Code: Introduction to Data Science
Автор: Аdаm Р. Таshmаn
Издательство: CRC Press
Год: 2024
Страниц: 386
Язык: английский
Формат: pdf (true)
Размер: 17.4 MB

The breadth of problems that can be solved with Data Science is astonishing, and this book provides the required tools and skills fot a broad audience. The reader takes a journey into the forms, uses, and abuses of data and models, and learns how to critically examine each step. Python coding and data analysis skills are built from the ground up, with no prior coding experience assumed. The necessary background in Computer Science, mathematics, and statistics is provided in an approachable manner. This text uses Python because it is one of the most popular languages for Data Science, it supports the tasks we need to accomplish, and it is relatively easy to learn. It will teach Python by diving into problems with data and solving challenges. It will not methodically catalog what each operator does, what each control structure looks like, and so forth. There are excellent references for learning in this way, with suggestions given later. Finally, the book includes many exercises across different topics, with the hope that they are interesting and motivating. Each step of the Machine Learning lifecycle is discussed, from business objective planning to monitoring a model in production. This end-to-end approach supplies the broad view necessary to sidestep many of the pitfalls that can sink a Data Science project.

Название: Data Analytics for Business: AI-ML-PBI-SQL-R
Автор: Wоlfgаng Gаrn
Издательство: Routledge
Год: 2024
Страниц: 283
Язык: английский
Формат: pdf (true)
Размер: 29.2 MB

We are drowning in data but are starved for knowledge. Data Analytics is the discipline of extracting actionable insights by structuring, processing, analysing and visualising data using methods and software tools. Hence, we gain knowledge by understanding the data. A roadmap to achieve this is encapsulated in the knowledge discovery in databases (KDD) process. Databases help us store data in a structured way. The structure query language (SQL) allows us to gain first insights about business opportunities. Visualising the data using business intelligence tools and Data Science languages deepens our understanding of the key performance indicators and business characteristics. This can be used to create relevant classification and prediction models; for instance, to provide customers with the appropriate products or predict the eruption time of geysers. Machine Learning algorithms help us in this endeavour. Moreover, we can create new classes using unsupervised learning methods, which can be used to define new market segments or group customers with similar characteristics. Finally, Artificial Intelligence (AI) allows us to reason under uncertainty and find optimal solutions for business challenges. The material was especially designed for Business Analytics degrees with a focus on Data Science and can also be used for Machine Learning or Artificial Intelligence classes. Data Science languages such as R provide a platform to access the state-of-the-art methods for analysing business data.

Название: Data Science With Rust: A Comprehensive Guide - Data Analysis, Machine Learning, Data Visualization & More
Автор: Науdеn Vаn Dеr Роst, Аlicе Sсhwаrtz
Издательство: Reactive Publishing
Год: 2024
Страниц: 460
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Dive into the world of Data Science with "Data Science with Rust," your comprehensive guide to mastering data analysis and Machine Learning using Rust’s powerful and type-safe code. Whether you're a beginner eager to step into the Data Science arena or a seasoned professional looking to leverage Rust's unparalleled efficiency and safety features, this book is designed for you. Hayden's unique blend of expertise in psychology, SEO, marketing, and technology imbues this guide with insights not found in typical technical manuals. It's not just about coding; it's about understanding data's narrative and extracting meaningful insights with precision and flair. Rust, a language born out of the relentless quest for performance coupled with unparalleled safety, presents a paradigm shift so profound that it redefines our expectations from modern programming languages. "Data Science with Rust" is more than a book; it's a journey into the future of data analysis. With Hayden's charismatic and caring guidance, you'll not only master Rust's syntax and its applications in Data Science but also learn to think like a data scientist, with a high degree of creativity, empathy, and strategic insight.

Название: Graph Data Science with Python and Neo4j: Hands-on Projects on Python and Neo4j Integration for Data Visualization and Analysis Using Graph Data Science for Building Enterprise Strategies
Автор: Тimоthу Еаstridgе
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 204
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB

Graph Data Science with Python and Neo4j is your ultimate guide to unleashing the potential of graph Data Science by blending Python's robust capabilities with Neo4j's innovative graph database technology. From fundamental concepts to advanced analytics and machine learning techniques, you'll learn how to leverage interconnected data to drive actionable insights. Beyond theory, this book focuses on practical application, providing you with the hands-on skills needed to tackle real-world challenges. Python and Neo4j are both terrific tools for graph data science. Each offers powerful tools and frameworks to analyze interconnected data. In theory, it is possible to implement end-to-end graph data science with either Python or Neo4j individually. However, in practice, it is much simpler to leverage both tools together to maximize the value of each tool. Python has become one of the most popular programming languages in the data science community due to its rich ecosystem of libraries and packages, allowing all users to leverage the incredible work of others and jumpstart their analysis. Python comes with several integrated development environments (IDEs) that facilitate code visualization, allowing users not only to view and debug their code but also to create graphs, charts, and various other visual representations of data.

Название: Python для сложных задач: наука о данных, 2-е международное издание
Автор: Джeйк Baндep Плac
Издательство: Спринт Бук
Год: 2024
Страниц: 592
Язык: русский
Формат: pdf
Размер: 13.2 MB

Python — первоклассный инструмент, и в первую очередь благодаря наличию множества библиотек для хранения, анализа и обработки данных. Отдельные части стека Python описываются во многих источниках, но только в новом издании «Python для сложных задач» вы найдете подробное описание: IPython, NumPy, Pandas, Matplotlib, Scikit-Learn и др. Специалисты по обработке данных, знакомые с языком Python, найдут во втором издании решения таких повседневных задач, как обработка, преобразование и подготовка данных, визуализация различных типов данных, использование данных для построения статистических моделей и моделей машинного обучения. Проще говоря, эта книга является идеальным справочником по научным вычислениям в Python. Эта книга не планировалась как введение в язык Python или в программирование вообще. Я предполагаю, что читатель знаком с языком Python и знает, как определять функции, присваивать значения переменным, вызывать методы объектов, управлять потоком выполнения программы и решать другие простейшие задачи. Она должна помочь пользователям языка Python научиться применять инструменты исследования данных, имеющиеся в языке Python, — библиотеки, упоминающиеся в следующем разделе, и сопутствующие инструменты — для эффективного хранения и анализа данных и извлечения из них полезной информации.

Название: Data Science for Neuroimaging: An Introduction
Автор: Аriеl Rоkеm, Таl Yаrkоni
Издательство: Princeton University Press
Год: 2024
Страниц: 393
Язык: английский
Формат: pdf (true)
Размер: 12.6 MB

Data Science methods and tools―including programming, data management, visualization, and Machine Learning―and their application to neuroimaging research. As neuroimaging turns toward data-intensive discovery, researchers in the field must learn to access, manage, and analyze datasets at unprecedented scales. Concerns about reproducibility and increased rigor in reporting of scientific results also demand higher standards of computational practice. This book offers neuroimaging researchers an introduction to Data Science, presenting methods, tools, and approaches that facilitate automated, reproducible, and scalable analysis and understanding of data. Through guided, hands-on explorations of openly available neuroimaging datasets, the book explains such elements of Data Science as programming, data management, visualization, and Machine Learning, and describes their application to neuroimaging. Readers will come away with broadly relevant Data Science skills that they can easily translate to their own questions. Note that this book is not meant to be a general introduction to programming. We are going to spend some time introducing the reader to programming in the Python programming language (starting in chapter 5; we will also explain specifically why we chose the Python programming language for this book), but for a gentler introduction to programming, we will refer you to other resources. There are a few different Python software libraries that visualize data. We will start with a library called Matplotlib.

Название: Data Science: A First Introduction
Автор: Тiffаnу Тimbеrs, Тrеvоr Саmрbеll, Меlissа Lее
Издательство: CRC Press
Серия: Data Science Series
Год: 2022
Страниц: 443
Язык: английский
Формат: pdf (true)
Размер: 30.1 MB

Data Science: A First Introduction focuses on using the R 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. The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows. Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for Data Science projects. The use of Jupyter notebooks for exercises immediately places the student in an environment that encourages auditability and reproducibility of analyses. The integration of Git and GitHub into the course is a key tool for teaching about collaboration and community, key concepts that are critical to Data Science. The book 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 in the University of British Columbia’s DSCI100: Introduction to Data Science course.

Название: Calculus for Data Science
Автор: Науdеn Vаn Dеr Роst, Vinсеnt Вisеttе
Издательство: Reactive Publishing
Год: 2024
Страниц: 338
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

In the rapidly evolving world of Data Science, proficiency in calculus serves as the foundation for tackling complex analytical challenges. "Calculus for Data Science" is the follow-up to the acclaimed best-seller that introduced you to the basics, now taking you on a deeper dive into the advanced techniques that will elevate your understanding and application of calculus in the Data Science field. This comprehensive guide is meticulously crafted for professionals who have grasped the essentials and are eager to extend their expertise into more sophisticated realms. With an emphasis on real-world applicability, "Calculus for Data Science" bridges the gap between theoretical constructs and their practical utility in data analysis, Machine Learning, and beyond. In Data Science, computational skills are as important as mathematical ones. A basic understanding of programming, particularly in languages like Python or R, will greatly enhance your ability to apply the concepts learned. Familiarity with data handling and manipulation techniques is also advantageous. Whether you are a student embarking on a Data Science career, a professional seeking to deepen your mathematical expertise, or a curious mind eager to explore the intersection of calculus and data, this book is for you.