Название: Software Engineering for Data Scientists: From Notebooks to Scalable Systems (Final)
Автор: Саthеrinе Nеlsоn
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 400
Язык: английский
Формат: pdf, epub (true)
Размер: 11.2 MB

Data Science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to Data Science. All the code examples in this book are written in Python, and many of the chapters describe Python-specific tools. In recent years, Python has become the most popular programming language for Data Science. Python has an extremely solid set of open source libraries for data science, with good backing and a healthy community of maintainers. Large trend-setting companies have chosen Python for their main ML frameworks, including TensorFlow (Google) and PyTorch (Meta). Because of this, Python appears to be especially popular among data scientists working on production Machine Learning code, where good coding skills are particularly important. In my experience, the Python community has been friendly and welcoming, with many excellent events that have helped me improve my skills. It’s my preferred programming language, so it was an easy choice for this book. This book is aimed at data scientists, but people working in closely related fields such as data analysts, Machine Learning (ML) engineers, and data engineers will also find it useful.

Название: Spatiotemporal Data Analytics and Modeling: Techniques and Applications
Автор: Jоhn А, Sаthееsh Аbimаnnаn, Еl-Sауеd М. Еl-Аlfу
Издательство: Springer
Серия: Big Data Management
Год: 2024
Страниц: 253
Язык: английский
Формат: pdf (true), epub
Размер: 18.9 MB

With the growing advances in technology and transformation to digital services, the world is becoming more connected and more complex. Huge heterogeneous data are generated at rapid speed from various types of sensors. Augmented with Artificial Intelligence and Machine Learning and Internet of Things, latent relations, and new insights can be captured helping in optimizing plans and resource utilization, improving infrastructure, and enhancing quality of services. A “spatial data management system” is a way to take care of data that has something to do with space. This could include data such as maps, satellite images, and GPS data. A temporal data management system is a system designed to manage data that has a temporal component. This could include data such as weather data, financial data, and social media data. Some advanced techniques used in spatial and temporal data management systems include geospatial indexing for efficient querying and retrieval of location-based data, time-series analysis for understanding and predicting temporal patterns in datasets like weather or financial trends, machine learning algorithms for uncovering hidden patterns and correlations in large and complex datasets, and integration with Internet of Things (IoT) technologies for real-time data collection and analysis. These techniques, augmented with Artificial Intelligence, enable the extraction of latent relations and insights, thereby optimizing plans, improving infrastructure, and enhancing the quality of services. This book provides essential technical knowledge, best practices, and case studies on the state-of-the-art techniques of Artificial Intelligence and Machine Learning for spatiotemporal data analysis and modeling. It can be used as academic reference at graduate level or by professionals in science and engineering related fields such as Data Science and engineering, Big Data analytics and mining, Artificial Intelligence, Machine Learning and Deep Learning, cloud computing, and Internet of Things.

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: Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications 2nd Edition
Автор: Lаurа Iguаl, Sаnti Sеguі
Издательство: Springer
Серия: Undergraduate Topics in Computer Science
Год: 2024
Страниц: 255
Язык: английский
Формат: pdf
Размер: 10.1 MB

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of Data Science. The coverage spans key concepts from statistics, Machine Learning/Deep Learning and responsible Data Science, useful techniques for network analysis and natural language processing (NLP), and practical applications of Data Science such as recommender systems or sentiment analysis. This book includes three different kinds of chapters. The first kind is about Python extensions. Python was originally designed to have a minimum number of data objects (int, float, string, etc.); but when dealing with data, it is necessary to extend the native set to more complex objects such as (NumPy) numerical arrays or (Pandas) data frames. The second kind of chapter includes techniques and modules to perform statistical analysis and Machine Learning. Finally, there are some chapters that describe several applications of Data Science, such as building recommenders or sentiment analysis. The composition of these chapters was chosen to offer a panoramic view of the Data Science field, but we encourage the reader to delve deeper into these topics and to explore those topics that have not been covered: big data analytics and more advanced mathematical and statistical methods (e.g., Bayesian statistics). This book is addressed to upper-tier undergraduate and beginning graduate students from technical disciplines. Moreover, this book is also addressed to professional audiences following continuous education short courses and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required. Code programming in Python is of benefit.

Название: Cognitive Analytics and Reinforcement Learning: Theories, Techniques and Applications
Автор: Еlkkiуа R., Subrаmаniуаswаmу V.
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 384
Язык: английский
Формат: epub
Размер: 10.1 MB

The combination of cognitive analytics and Reinforcement Learning is a transformational force in the field of modern technological breakthroughs, reshaping the decision-making, problem-solving, and innovation landscape; this book offers an examination of the profound overlap between these two fields and illuminates its significant consequences for business, academia, and research. Cognitive analytics and Reinforcement Learning are pivotal branches of Artificial Intelligence. They have garnered increased attention in the research field and industry domain on how humans perceive, interpret, and respond to information. Cognitive science allows us to understand data, mimic human cognitive processes, and make informed decisions to identify patterns and adapt to dynamic situations. The process enhances the capabilities of various applications. Readers will uncover the latest advancements in AI and Machine Learning, gaining valuable insights into how these technologies are revolutionizing various industries, including transforming healthcare by enabling smarter diagnosis and treatment decisions, enhancing the efficiency of smart cities through dynamic decision control, optimizing debt collection strategies, predicting optimal moves in complex scenarios like chess, and much more. The book caters to a diverse audience that spans academic researchers, AI practitioners, data scientists, industry leaders, tech enthusiasts, and educators who associate with Artificial Intelligence, data analytics, and cognitive sciences.

Название: Data Science Fundamentals with R, Python, and Open Data
Автор: Маrсо Сrеmоnini
Издательство: Wiley
Год: 2024
Страниц: 461
Язык: английский
Формат: epub
Размер: 10.1 MB

Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start Data Science projects. Organized with a strong focus on open data, Data Science Fundamentals with R, Python, and Open Data discusses concepts, techniques, tools, and first steps to carry out Data Science projects, with a focus on Python and RStudio, reflecting a clear industry trend emerging towards the integration of the two. The text examines intricacies and inconsistencies often found in real data, explaining how to recognize them and guiding readers through possible solutions, and enables readers to handle real data confidently and apply transformations to reorganize, indexing, aggregate, and elaborate. This book is full of reader interactivity, with a companion website hosting supplementary material including datasets used in the examples and complete running code (R scripts and Jupyter notebooks) of all examples. Exam-style questions are implemented and multiple choice questions to support the readers’ active learning. Each chapter presents one or more case studies. Data Science Fundamentals with R, Python, and Open Data is a highly accessible learning resource for students from heterogeneous disciplines where Data Science and quantitative, computational methods are gaining popularity, along with hard sciences not closely related to Computer Science, and medical fields using stochastic and quantitative models.

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: Advancement of Data Processing Methods for Artificial and Computing Intelligence
Автор: Sееmа Rаwаt, V. Аjаnthа Dеvi, Рrаvееn Кumаr
Издательство: River Publishers
Серия: River Publishers Series in Computing and Information Science and Technology
Год: 2024
Страниц: 431
Язык: английский
Формат: pdf (true)
Размер: 36.7 MB

This book emphasizes the applications of advances in data processing methods for Artificial Intelligence in today's fast-changing world, as well as to serve society through research, innovation, and development in this field. This book is applicable to a wide range of data that contribute to data science concerns and can be used to promote research in this high-potential new field. People's perceptions of the world and how they conduct their lives have changed dramatically as a result of technological advancements. The world has been gripped by technology, and the advances that are being made every day are undeniably transforming the planet. In the domains of Big Data, engineering, and Data Science, this cutting-edge technology is ready to support us. Artificial Intelligence (AI) is a current research topic because it can be applied to a wide range of applications and disciplines to solve complicated problems and find optimal solutions. In research, medicine, technology, and the social sciences, the benefits of AI have already been proven. Data Science, also known as pattern analytics and mining, is a technique for extracting useful and relevant information from databases, enabling better decision-making and strategy formulation in a range of fields. As a result of the exponential growth of data in recent years, the combined notions of Big Data and AI have given rise to many study areas, such as scale-up behaviour from classical algorithms.